Tableau is not just a BI tool.
It is a way to tell a story
to your company stakeholders
based on your company data.
Hi all I welcome you
to this full course
session on Tableau
and what follows is going to be
a tableau training for beginners
which covers all the concepts
that you need to start out
with this technology.
But before we begin,
let's look at our
agenda for today,
so Going to start out
with some Basics.
We're going to talk about
data visualization and Tableau.
And why do we need both
of these followed
by which we are going
to discuss installation
of the Tableau desktop tool.
Now, this is the tool
that you are going to be using
throughout the course
of this video.
Next.
We are going to talk about
data visualization using Tableau
and then we are going
to discuss an important concept
called visual perception.
We're also going to talk
about how Tableau incorporates
these visual Perceptions
in Its components to give you
Optimum data visualization.
Then we're going
to talk about charts
and graphs in Tableau
and how you can enhance
your data using these then
we're going to incorporate it
into one single dashboard
using the Tableau desktop app.
Now up to this point
all the topics
that we're going to talk about
are going to be majorly focused
on data visualization.
Now this point onwards
the topics we are going
to discuss are going to be more
about data aggregation.
So first stop in that
is Tableau functions.
Now, this is tableau version
of Dax next we're going
to discuss level
of detail another very important
very fundamental concept.
Then we are going
to discuss parameters
and Tableau followed
by which we are going to talk
about data blending in Tableau
and how it is different
from SQL joins moving on.
We're going to talk about
The Career aspect in Tableau.
So we have a module called
how to become a tableau developer
which discusses the roles
and responsibilities of a tableau
developer. To cap of the session,
We have some interview questions
for you based on Tableau.
Now your instructors
for this particular session is
going to be Reshma and Upasana.
They're going to take you
through step by step
through all the sessions
of this Tableau tutorial.
Also go ahead and kindly
hit the Subscribe button.
So you never miss an update from
the Edureka YouTube channel.
Hey, everyone.
this is Reshma from Edureka.
And today we'll be
learning about Tableau.
So let us first understand
what is data visualization.
Well data visualization
means representing your data
in a pictorial form.
It may be in the form of a graph
or a bar diagrams
or a different kind of charts
and visualization allows
us to visual access
to huge amounts of data
in easily digestible visuals
because let's say you
might have got all the data
and Excel sheets.
You have got all that with you.
You have got the text.
You've got the
numbers and everything.
But if you just view
only numbers and text,
you might not get
a whole picture out of it.
So you need to represent
in a manner
so that you can understand
it better and visualization
enables you to have
a well-defined overview
of your entire data
and the simpler
your visualization is
the more insights
and inferences you
can make from it.
So simple representations
are the most As powerful ones
so that is why we need
data visualization to understand
our data in a better way
and these can be used
for analysis of data
to make future predictions.
And this is highly used
in solving business problems.
And the two that we
are going to learn today,
which is Tableau This is
highly used in bi
so this is data visualization.
Now, let's take an example.
So here we have got the X
and Y coordinates
of different point
and these represent.
Resent a line.
So the example
that is before you this
represents the data points
in four quadrants.
You have got the X
and Y coordinates.
So if you see this data,
you'll see that there is not
much differences in the numbers
and you might think
that when you plot
this data points,
this might look the same
but now let us take a look.
So when you picture
I said you'll see
how different they are
from each other.
They are not similar at all.
Even though the numbers
look similar so that is
why you need to visualize.
It to understand it.
You will not get
the whole picture
when you are seeing just numbers
but when you plot it, you
can see how different they are.
So that is why data
visualization is important and
that is why it is highly used
because picture Rising your data
and analyzing will be
so much easier
when you plot it and you see
the behavior of your data
and that is how you
can make future predictions.
And that is why
data visualization is so popular
and people have been using
it all around the world.
So I hope you
have understood the importance
of data visualization
from this example.
So let us move forward
to our next topic and let
us see the scope
of visual analysis X so
visual analytics is used widely.
It can be used
for informational it except for
geospatial analysis scientific
analysis takes knowledge
Discovery data management
and knowledge representation
for presentation production
and dissemination this
used for a cognitive
and perceptual science.
For interaction and there
are many more usage of it.
And why do we use visual
analysis X because it helps
us to make better decisions
because when you can study
the behavior of it,
you can make better analysis
of the data and you
can take decisions
which will be beneficial
for your company
and you can make
future predictions accordingly
and plan everything
and you can also get
a better sense of risk
because when you can make
future predictions correctly,
obviously the risk
factor, Go down,
and this is very much beneficial
for your company
and you can also build
better customer relationships
better key stratigic initiative
and better financial performance
because the risk
factor will be low.
You can save a lot of money
by studying the data
by representing it
and it will give you
a brief idea of everything.
So this is the entire scope
of visual analysis.
Now, let us understand
how does data
visualization actually works.
So the first thing you
need to visualize the data,
Data is a data set.
Now your data set can be
in form of text file.
It can be any kind
of flat file Excel sheets.
You can also connect
to any server or any database.
So and not just one
you can integrate
and connect different
data sets together.
And then you analyze that data
according to the parameters
and then you carry out
the visualization of
how you want to represent
what you have analyzed.
This is a brief overview
of how data visualization
actually works so there.
Analyzing you use
different formulas use
different algorithms to analyze
it and for visualization,
you can choose different charts
or Maps graphs or anything
that you want whichever fits
for your data set and now
let us understand why Tableau
if you want to
visualize your data,
why go for Tableau,
let us understand it by looking
at the features of tableau.
So the first feature is
that it is very flexible.
You can connect it
to any kind of data.
It consists of amount
of optimize data
connectors for databases.
You can connect it
to an Excel file.
You can connect it
to a text file even
collected to a Json file
or any kind of server.
You can connect it
to a tableau server
a Microsoft SQL Server
Oracle Amazon redshift
and many more
and it provides you
with a very intuitive platform.
Now according to Gartner.
Tableau is actually Considered
the gold standard for intuitive
interactive visual analysis
and an established
Enterprise platform
and you can represent
your data in any way
that you want the visuals
that tableau gives you a very
interactive you can tweak them
around you can play
around those graphs
and the different charts
that you make in Tableau
and you can visualize
your data in many ways.
And also it has very
quick production them.
It takes only a few seconds
for Tableau to wait
the visualization
that you want for your data.
And that is the reason
why Tableau has been
among the top charts
when it comes
to a visual analogy takes
and a presentation tool
and let me tell you
that and Tableau
has managed to be
in the Gartner's magic quadrant
from years from now
and now it is treated
as the top interactive tool
that is used in bi standing
above click View and other tools
and that is why we
should all start using Tableau
and it is very fun to play
around with Tableau.
Let me tell you.
Show you that in a while
how we can make
different analysis of data
by representing it visually,
so now let us move on
to our next topic and let
us see in detail.
What is Tableau?
So Tableau is a software company
which produces interactive data
visualization products focused
on business intelligence
and a lot of companies
and almost all the big giants
are using Tableau
for business intelligence and
for data analysis purpose.
So with Tableau,
you can create
and distribute interactive
and shareable dashboards,
which depict the trends
variations and density
of data informs of graphs
and charts and these software
also enables you data
blending real-time collapse,
which is what makes
Tableau stand out
from all its competitors
and it is very unique you
can use it for business purposes
for academic purpose
or for any purpose.
Whichever you want
to it will help you to do all
the visual analogy takes That
you want and we're doubling
you don't have
to spend much time.
Tableau will do all
the work for you.
So you don't have
to Wrangle around the data
that much you don't have
to scratch your head
trying to figure out
how you should represent.
Your data.
Tableau has got lots
of options available.
You can choose
how you want to depict it
and it will do the rest for you.
So this is why Tableau
is popular and this is
what Tableau enables you to do.
So now let us understand more
about Tableau by looking
at the architecture of Tableau.
So in the left hand side,
you can see the data sources
that you can connect Tableau
to and to connect with Tableau.
It uses data connectors
and Tableau consists
of amount of optimize
data connectors for databases.
There are also common
odbc connectors designed
for any systems
without a native connector
and it offers two modes
in support of interacting
with data first,
you can have a live connection
or in memory connection.
I'm clients can switch among
alive and in memory connection,
whatever they desire.
So this is the analyzing part.
So what happens
in a live connection is
that the data connectors
of Tableau control your
available data infrastructure,
but transferring Dynamic SQL
or MDX statement straight
into the source database
except importing every data
if you have provided in a quick
and analytics optimize database
such as vertica,
then you can get the advantages
of that investing by connecting.
Live on to your data and this
leaves the detailed data
in the source system
and sends the aggregate outcomes
of query to the Tableau.
And in addition.
This means that Tableau
can effectively utilize
unlimited amounts of data.
Well in fact a blue is the front
and an Alex client
to several of the large
databases in the world
and it has optimized
every connector to
receive the advantage
of unique characteristics
of every data source,
and this is the visualization
that you can produce using
Tableau you can make it.
A workbook for Tableau readers,
you can also
make static readers.
Now you cannot work more
in this static readers.
This will just represent
the visualization
that you produced
and you can also produce
visualizations for web
and mobile users
by using Tableau server.
Now.
Let me tell you how you can use
in memory connection.
So in memory connection
is a very fast data engine
to optimize your analysis.
You can connect your data
and after that
with one click you
can extract your data.
To get in memory in Tableau
and tableau data engine fully
consumes your entire system
to attain fast queries answers
on millions of rows of data
on commodity hardware and
since the data engine
can use disk storage as well.
And as well as RAM
and cache memory.
Let me tell you
that it is not confined
with the quantity
of memory on a system
and it is not essential
that an entire data
set can be loaded
into memory to attain
its performance objectives.
So what happens
when a user opens a view
in a client device?
So when a user opens a view
the user begins a session
on the Tableau server
and then the application
server thread begins
and then verifies
the permissions there are
security protocols defined
for a particular user
and then the user
can have an access
to the view created by Tableau.
And this is how the
Tableau architecture Works
in order to connect
to your data source
in order to analyze it
and finally providing
you with a visual.
Data or graph
or any kind of visualization
that you chose to be.
So let us move on
and let me tell you
how to install Tableau now
installing Tableau is very easy.
You can go to
the Tableau website
and download the exe file.
Just run that file
click on install.
It takes minutes
to install Tableau
or sometimes it can also
get installed in a few seconds.
After you have
installed Tableau desktop,
the latest version
of Tableau desktop is 10.2.
So you can install
that and after installing
it will ask you to register
to activate your version.
So you'll get a license key.
You can purchase
the license key.
And if you are a student
and you wanted
for Academy purpose you
can get it for free for a year.
So you just have to go
through the registration process
and there you have
Tableau ready to use.
Now let us go ahead
and understand Tableau
little bit more.
Now.
Let us understand
how to connect to different
data sources in Tableau.
So when you open Tableau,
the first thing you'll see
is the connect option.
Now I can connect to any files.
These are the sources
that I told you about.
You can connect it
to Excel files to text files
Json files or any kind
of server as well.
So if you already have
a data set in your system,
you can just go browse
on to the file.
Location and you can open that.
So in Tableau,
you will have sample data set
which is the superstore
and you can rename it
if you want to and
when you load that data set,
you'll see a preview
of all the different fields
and attributes of the data set
that you have so you can see
that these are
the attributes the order ID
or the date ship date
ship mode customer name segments
and this can all be viewed even
before you open
your worksheet now
Tableau has also
Different data types,
these are the data types
that will deal
with there are Boolean
which contains true or false.
There are dead values.
There are date
and time stamp values
the date values just
have the date the month
and the year and indeed in time.
There is also the timestamp
in this format.
This is the hours minutes
seconds and AM and PM,
you can also represent
geographical values.
So there are geospatial data
when you It feels
like city or state
that is related to a geography
Tableau will detect it and
it you can represent it
in geographical values.
You can create a view with maps
which is very interactive.
And which is also
very popular in Tableau.
Tableau also uses whole numbers
and decimal numbers
and also text
and strings and all
this data types
are represented by symbols.
You can see over here
that text and string values
are represented by ABC
the Values are represented
with the calendar icon the date
and time values are represented
with a calendar icon.
But with the clock the numerical
values are represented
with a hash symbol
the Boolean values artiste 5f
which is true or false
and geographical values
are represented with a globe
with latitudes and longitudes.
And the best part
about Tableau is
that it auto detects
all the data types.
You don't have to specify
which data type is what
but if you want to you
can do that as well.
You can explicitly Define
if it's a number or a string.
You can do that in Tableau.
Now, let us see
the Tableau desktop UI so
when you open your worksheet,
this is what it looks like
so you can see
there are dimensions
and measures the dimensions are
usually the text Data
you can see it's
ABC Means it's a text
or it can be a date.
The measures are usually numbers
will know more about dimensions
and measures later
on this tutorial
and over here with this toolbar.
You can decide how you want
to represent your data.
You can label your data.
You can use tool tips,
which will help you
to hover over your data
and see the details.
You can include what details
you want to represent
by just dragging
and dropping the dimensions
in detail section.
You can play around with colors.
Colors about how you want
to represent your data
and this is the rows
and column section.
So you can just drag
and drop items over here
and here you can see the
pictorial representation of data
and this is called a canvas
and this is your workspace.
This is for creating
new workbooks or dashboards
will explore more
about Tableau you
I when I show you
the demo so now let
us move on and let us understand
about dimensions and measures
so Mention is a field
that is an independent variable
and the data types
could be the strings.
It could be Geographic
locations numbers daytime
anything and Tableau
guesses the data type
according to the mention names.
So when you specify region
it might take it as a text
or it might also take it
as a geographic location.
You can Define
that explicitly as well.
So now it is representing
it as text.
But if you want to represent
it as a geographic location,
And you can specify and change
the data type of these field
or this Dimension
and dimensions are actually used
for detailing your data.
I'll tell you how but first let
me tell you what our measures
and in measures all
the data types are numbers
and these are
the inbuilt data Dives
the latitude and longitude.
This can be
used for representing
Geographic locations,
but mostly they're all numbers
as you can see over here.
And to represent
a measure you always
need a dimension and dimension.
Like I was telling you
it helps to detail your data.
Now when you see just
measures let's consider sales.
So you just see a number
the sales is let's say 10,000
but that doesn't give you
a whole picture of anything.
But when you say it
like the sales by region
or sales by a unique product ID
and this helps you to add detail
in a representation of data.
That is how you can get
a clear picture of your data
when you represented
by different dimensions.
And this is what is used
for analyzing your data.
So this is what dimensions
and measures are now
this is the show me data.
So this pain over here shows you
how you want to
represent your data.
So there are a number
of options available.
You can show your data
by representing it
in a pie chart by heat maps
by bar diagrams.
There are different styles
of representing in bar graphs.
Or you can also represent it
in the geographical map
when you choose a data
set it will automatically
highlight the data
that you can use
to represent it.
If you see over here some
are blurred and some are not so
if it is blurred
you can understand
that your data set is
not compatible to use
these kind of line graphs.
You can use a bar graph or you
can use a pie chart for that
but not line chart maybe
because your data
that you're using is
not compatible to it.
So now let us move on
and understand more
about this visualisations.
So the first ones are
graph you can represent
your data in bar graphs
or line graphs and you can also
represent both of them together.
You can choose to have
a horizontal bar graph
or a vertical graph
that you want.
You can play around with colors.
If you want to show different
sales by different months.
Let's say and you want
to represent it with And graphs
you can use different colors
for different lines
for different months.
And if you want to see
two different fields,
if you want to compare
two different fields together,
you can use a dual axis graph
also so over here,
you can see that we
have represented this prophet
and the shipping cost together.
Now when I visualize
it this way,
this was the graph
for shipping cost and this
was the graph for profit.
Now when you
represent it together,
you can see that whenever
the shipping costs.
Has increased the prophet
also has increased so this
gives you a clear picture.
Right and there is
a perfect correlation
with shipping cost and profit.
So this is how you can make
analysis of your data
and now you can make
future predictions.
If I increase my shipping cost.
I will earn more profit
definitely so that is why
visualizations are important
and you need to understand also
that how you should visualize
your data and the next is
the geographical graph now
if Viewing the sales by regions.
You can see it and
a geographical graph like that.
You can pinpoint the areas
where you're getting
the maximum amount of sales
and you can also have
an area graph with dual access
has now this are the sales
and profit dual-axis graph
that you can see over here
and there are many more ways
of visualizing your data.
Let us see some more now,
this is one of the most popular
visualisations in Tableau the
which is called the heat map.
Now colors are very important
in heat map and heat Maps.
The denser is the color
the more value.
It represents.
You can see the profit
if it's red save the colors
get darker over here.
It means that it is
a negative aspect
so you can see over here
that this tables category.
The sales is very bad
or the profit is very bad
because it has
the darkest color of it.
And when you see
in case of phones,
which is the lightest colors,
it means the profit.
And sales are very high
in case of phones.
The next one is
the tree map in tree maps.
You can represent it
in rectangular forms
and also you can play around
with colors over here.
So the darker the green the more
is the prophet and you can see
in case of copiers.
It is the highest
and in tables it is not much
and you can separate your data
using rectangles when
you're using a tree map now,
let us understand
which visualization to apply
with what Kind of data sets.
So in the left hand side.
This is the visualization
that you should choose
and this represents
the kind of data set
that you're using.
So let's say that
if you're using a data set
that contains this
continuous values,
you can use a bar graph for that
and for continuous Dimensions,
you should use a line graph.
And if you want to represent
two measures together
that we just saw and
if you want it for comparison,
it is preferable
that you use a dual axis graph.
And if you want to plot measures
on geographical map
if you want to see
the sales by region
or anything or profit
by region or if there is
a geographical field involved,
it is better
that you use a geographical
graph in that case.
And again,
if you want to compare data
between different regions
or compare different to measures
according to different regions,
then you can use the area graph
with dual accesses or basically
when you have got a feel
like There is a count
and there is a measure
or the amount of density.
Then you can use
a tree map for that
because with tree map you
can represent the quantity
as well as the density
of particular measure.
So you'll get that idea
when you are using
Tableau for a while,
you'll understand which one
will be better.
You can also hit and try methods
and you can analyze
yourself which visualization
will be better
for your data set,
but this is just
to give you a brief idea
because your data set.
We'll definitely contain at
least one or two different kinds
of data among these
and now let us understand
functions in Tableau.
Now, you can use
different functions to join
different data sets.
If you want to combine columns,
you can use joins
and it uses all
the SQL joins that you
might have studied about.
You can use an inner join
a full outer join left
join right joint.
And this will combine
different columns together.
You can also have
a union to Fine Rose
but the constraint
or the condition is
that the data fields
or the attributes should be say
when you are combining
different rows together.
And this is how you can combine
different data sources together
by either join or Union.
You can also sort
your data Accord with
the Tableau sort function.
Let's say that you wanted
in an increasing or decreasing
order you want to maybe see
that which one has
the maximum amount of sales
and you wanted on the top
so you can use
The increasing order
for that also and it has
different sorting techniques.
You can choose any sort function
that you want to represent
your data and set
is a type of filter
which we can set a condition
for displaying values.
Let's say that we just want to
display just one kind of value.
For example over here.
Let's say that if you want
to represent discount,
which is greater than 10%
so we can use set
for that and this is
actually a collection
of Dimension members.
And you can also use
Tableau UI for forecasting.
So this is used for prediction.
When you have a set
of different Trends going on.
You can represent
it by a line graph
to represent a trend
and you can derive
a future prediction
or a future line graph
according to the graphs
that is represented
for different years
or different months
and you can use the line graph
to predict the future as well.
And this highly depends
on the values of graphs
and the different
Find some graphs
that you're using to represent.
The earlier values
This is highly used
in the business intelligence to
make predictions for Investments
or different purposes.
And if you want to highlight
something let's say
that you want to highlight
a particular Trend
or a want to highlight the ones
that has the maximum
amount of sales.
So this is what you can use it.
You can highlight it
with a particular color blur
out the other line graphs
or blur out the other day.
Diagrams or visualizations
that you created.
So this is one way also
how we can visualize your data.
You can also design visuals
for a particular device.
If you want it in your mobile,
you can select it
the device type tablet
or it is a mobile phone.
You can do that also
because the resolutions
of different screens
are different and you know
that mobile phones are small
the screen is smaller
than the computer or the tablet
that you're using.
So all the visualizations
that it created together
in the dashboard.
Might not fit into
because it might be too small
and you won't get
a proper visualization
so you can design it and you
can tweak the visualizations
according to devices as well.
So this is
how you can make different
visualizations in Tableau.
So let us first get introduced
to visual analysis.
So visual analysis means gaining
insights from a visual interface
and in order to gain
the right insight and knowledge.
Knowledge from a data source
we should be able to represent
the data visually as
accurate as possible.
So what do you see in front
of you is the wheel of how you
can represent data visually
in the most appropriate manner.
So the first step is
to acquire the data
or to understand what data
you're dealing with.
Then you should filter
out the data to select
the correct parameters,
which will be able
to represent your data
in a more insightful
and meaningful way.
Then you should take steps
to enhance your data to make
it visually attractive then
tune it Gained the inside
or to make the inference
that you want to
and finally deliver the data.
So this is the cycle
of visual analysis
or these are the steps
that you should follow
in order to represent
your data visually as
accurate as possible.
And now let us understand.
How do we perceive data Because
unless we understand the visual
perception of human beings,
we won't be able to understand
or we won't be able to make
the right choice of
how to represent our data.
So now let's do an exercise.
So what do you see
in front of you now is
Is a series of numbers
I'm asking you to count
the number of fives
in this sequence.
So let me tell you
that this is the same sequence
but you can see the difference.
I've only bowled out the five
and I've darken
the color of five
which was easier
which took less time
to count the number of fives.
Yeah, so definitely
the second one took lesser time
because this was
visually more perfect
or this was visually helpful
for us to count
the number of files.
Whereas here you might miss out.
Sighs because the letters
are quite jumbled and they're
like cramped altogether.
So this makes it harder but
when you pulled out the fives
and you enhance
the color of five,
you can see how easy this is
to grasp on the data.
So this is the thing
that you need to understand the
how your mind actually
perceives the data.
Now the first one
is called attentive learning.
This is where you have
to put on a lot of attention
to find out the data
or to gain insights or extract
the right Knowledge from it.
The next one is Very attentive.
This does not require
that much attention
like the one before
and this makes it very easier
for us to grasp on.
So this is the Baseline
that you need to understand
when you're trying
to represent your data try
to understand the ways
and the possibilities about
how you can represent your data
in a more informative way.
So I hope that you
have understood the purpose
of giving you this exercise
and to why I'm telling
you about attentive
and preattentive.
See the more you learn
with less effort
as beneficial, right?
So you have to find out ways
in how you can represent
or give more insights
and extract more knowledge
with just one visualization.
So now here is an example.
So the chart that you see
so I've represent the sales
of a particular company
and the sales
that they count are divided
into first quarter
second quarter third
quarter fourth quarter,
so they make an analysis
of their sales.
Dark Water so these
are the yearly graphs
or the yearly pie charts
for the consecutive three years
from 2014 15 and 16.
Now I have represented
this in a pie chart.
If you could see these two
almost looks same
if I asked you that question
that for the third quarter,
when did this company
have largest sale
whether it was in 2014 15 or 16?
I'm asking you for the third
quarter 2014 now tell me
the difference between 2015
and 2016 for the third quarter.
If you see it is very hard
to detect the size.
You have to observe it very
minutely then only you'll find
that 2015 is slightly
little bigger than slice
in 2016 and similarly
if I asked you for 2014
15 and 16 for the first quarter.
Can you tell me
which one of them
have the highest sales
for the first quarter
now this differences
in the slices are very hard
to detect you have to pay
a lot of attention.
Chin, again, this is
an attentive perception.
So this is another way
of representing the same thing.
So the gray bars
over here represent 2016 sales
the pink ones for 2015
and blue ones for 2014.
Now if you compare
and I have laid out
all the quarters together
to compare each of this.
So like I was asking
for first quarter,
which one was the highest
so you can clearly see
that 2015 had the highest
sales were in first quarter
whereas in In second
quarter 2016 had it
in third quarter 2014 and in
fourth quarter also 2014.
So this is very much easy.
Now I could just look
at this bar diagram
and answer me in just
one or two seconds, right?
Whereas when you were looking
at the pie chart it
took you more time.
So you can see
that making insights
from this diagram was
far more helpful and far more
quicker than the pie chart
that we just saw.
So this is the fundamental thing
that you need to understand
when Trying to use Tableau
to represent your data
because there are going
to be a lot of fields that
you'll be dealing with.
Your data set will have
numerous amount of fields.
You have to extract
the right ones.
You have to select
the right ones with which you
can represent more information
in just one visualization.
So now let us take a look
at the Tableau product family.
So the Tableau Software
that you'll be using
for data visualization is
the tablet desktop.
The Tableau desktop
has got different versions
that you can use.
So these are the Tableau reader
Tableau public Tableau server
and Tableau online.
So let me first tell you
about Tableau desktop
what you can do
with Tableau desktop.
So double desktop is
a self-service business in Alex
and data visualization
that anyone can use it
can translate pictures of data
into an optimized database.
Is it can connect
directly to data
from your data warehouse for
Life up-to-date data analysis,
it can perform queries without
writing a single line of code.
It can also import data
into tableaus Data engine
from multiple sources
and integrate them as well.
You can also
combine multiple views
in an interactive dashboard
with help of Tableau desktop.
And another version
of Tableau desktop is
the Tableau server.
Now, this is a more Enterprise
based Tableau desktop.
So Enterprises have
to buy a tableau server.
Over the large Enterprises
usually by Tableau server
and they use it
for publishing dashboards
with the Tableau desktop
and they can share them
throughout the organization
with a web-based tableau server.
What it does is
that it leverages fast databases
through the Life Connections.
Whereas Tableau
online over here.
This is a hosted version
of Tableau server,
and it makes business
intelligence faster and easier
than before you can publish
Tableau dashboards
with Tableau desktop,
and then you can share them.
Their colleagues or
friends and they're
like we come to Tableau reader.
Now.
This is just a readable version.
It is a free desktop application
and it will just enable
you to open and view
the visualizations build
in the Tableau desktop.
So someone else would be
building it you'll just
be able to open it
and view it you can filter
and drill down the data,
but you can't edit or perform
any kind of interactions.
This completely depends
on how the author
has represented it
so you cannot make any changes.
But you can only
use filters on that.
So these are the versions
that you get in the
Tableau product family.
And right now the latest version
of Tableau desktop is
the Tableau 10.2 which is
the best among them.
So you can directly
download Tableau desktop
and you can register it.
You'll get a license key or even
if you just want
to try it first,
they'll give you a trial version
which is for 15 days.
So you can use Tableau desktop.
It has got all the features
the Tableau server
is for Enterprise.
Ever see if you just wanted
for individual use you
don't have to buy
a tablet server
because it is also costly
so Tableau public is something
where you create visualizations,
but you cannot keep it with you.
You have to publish it
and everyone can see it.
So it's up to you.
I'll just suggest you to go
and download Tableau desktop
and I will advise you
to download the latest version
which is 10.2.
So we'll move on and let
us see why Tableau.
So these are the pros of Tableau
or the features of Tableau
because of which it
makes Tableau very easy
and very popular.
So the first thing
as I was already saying easy,
the first pro is its ease
of use will Tableau is
a very interactive tool.
It is very easy to learn
and very easy to use you'll know
that when I show you
how Tableau looks
like and how to make
visualizations with Tableau
so you can find all
the options in front of you.
It is very easy
to represent your data.
The only thing
that you need to know is How
to do it not how to use Tableau
but how to represent your data
so you have to be able to choose
the right options from Tableau.
So it's a direct connect
and go it means
that it can connect
to any kind of data source,
which I already mentioned
about and also you can make
different kind of connections.
You can extract the data
from a particular data source
and make changes on to it or
make visualisations out of it,
or you can make a live
connection with it.
It is also perfect
for mashups it Is
that you can join
different data sets together.
You can include
an integrated lot
of data sources all
together to analyze it
and represent them visually
because sometimes it
is not enough to just take
an account one kind of data set.
There might be similar data sets
which you want
to analyze together
and with Tableau you can do
that and the best
practices in a box.
So Tableau comes in package
with everything you need.
It has got different filters
if you want to sort your data,
it has got an option
for that too.
And there are
many other visualization.
Should options that you
can get with tablet.
You can represent
even geographical
locations with Tableau.
So everything that you might
need to represent your data
that is there in Tableau.
I'm pretty sure
that there will be
nothing that you say
that oh my God.
This is not there.
It would have been useful
if this was there to make
this visualization look good
because I'll tell you
you can use shapes.
You can represent it
with different shapes.
You can even download
your custom shapes
and represent it
in the visualization.
So that is why Tableau
has become so much popular
because of all these features
that Tableau has so
now let us learn
how to use Tableau.
So the first thing
that you need to learn is
to connect to a data source.
So when you open Tableau
first thing that you need to do
is connect to a data source
and you can connect it
to any kind of file
whether it's an Excel
file txt file,
Json file access files
spatial file statistical file,
if you also want to connect it
to a server you can connect it.
A tableau server
the Microsoft SQL Server
MySQL Oracle Amazon redshift
and there are even more options.
Well in Tableau,
you also have
a sample databases.
So if you're using Tableau
for the first time and you
don't have a data set yet,
you can use their own
default data set as well.
So there is a super
store data set
and I guess there are
two or three more
that you can use
and there are different ways
to connect to your data
so you can select
how you want to connect
with your data you
can connect T' life it means
that connecting directly
to your data
and the speed
of your data source
will determine the performance
you can import all the data
into tableaus Data engine
or you can import only some
of the data or the data
or the fields are parameters
that you might need in order
to make your visualisations.
So you can choose from any
of these three options.
So the second two options
can be also called as
in memory connection
and in order to make connections
to a database Blew
his God and amount
of optimize data connectors.
There are also common odbc
connectors designed for systems
without native connector
and it offers two modes
and supports of
interacting with data.
So as I told you the first
one is life connection
and the second one is in memory
where you can choose
to import all data
or import some of the data
that you might need
and one great thing
about connecting to data
with Tableau is
that you can switch
among a live connection
or an in-memory connection
whenever you want
to let me tell you
what happens in
a live connection.
So when you try to connect
a live with your data source
the data connectors
of Tableau control your
available data infrastructure,
and they transfer Dynamic SQL
or MDX statements straight
to the source database
except importing every data
and when you choose
to import data,
it presents a fast
in-memory data engine
to optimize for analytics.
You can connect your data
and after that
with just one click you
can extract your data to get in.
In oo, so what happens here is
that the tableaus data engine
fully consumes your entire
system to attain fast queries
and it can answer
on millions of rows
of data on commodity Hardware.
So these are the ways to connect
to your data and you
don't have to worry about
how the data connectors were.
You just have to worry about how
you're going to represent it.
So even if I'm saying
like a lot of complex
sentences don't worry,
all you have to do is just click
on your data source,
and then Tableau
will Extract all the data
that you want and you
can start working on it.
It is just a matter
of one or two clicks
and when you're connecting
two data sources,
it doesn't mean
that you can connect
it to only one.
I was already
talking about mashups
which means connecting
different data sources together.
And this is how you can connect
different data sources together.
So when you are trying
to include more
than one data source,
it will give you an option of
how you want to combine them.
So these are the joints
that are available you
might have seen This
in the SQL joins.
This is exactly the same.
You can either choose a left
join a right join inner join
and a full outer join.
So the left join means
everything from the data set
including the common one
from the other data set
the right joint means everything
from the second database
and the common ones
with the first database
the inner join means
only the common part
of both the two databases
and full outer join
means all the fields
of both the databases.
So this comes in very handy.
Handy, when you want to analyze
a bigger portion of something,
let's say that at one point
you are analyzing
about the cricket teams
of a particular country
and then you decide
that you have to analyze
about other sports too.
So there might be some feels
like player name or player ID,
which will be unique
or this field name
will be similar
but the data will be unique
from the two databases or more
so you can use joints like this.
So the Visualizations
that we have already
created before and if you
want to integrate the same
with the different data source,
you can do
that using the joints.
So if you go ahead and click
on the show me data beIN
on Tableau desktop,
you can find the visualizations
or the layout
of the visualizations of
how you can represent your data
so they can be in the form
of heat maps 3 maps and
bargue diagrams in pie charts
or even you
can represent it according
to geographical locations.
So you have
the Liberty to choose.
Is how you want to represent it?
But sometimes it may happen
that some of the options might
be unavailable for your data
and this completely depends
on the data source that
you're dealing with.
So for some data
if there are no fields
for Geographic locations,
you can't represent it
in the geographic locations
or if there is a data
or the parameters
that you're dealing
with does not specify
anything about density.
You can't use heat Maps.
So I'll be showing you
when to use which of the maps
and how you can choose
the current visualization.
Relation are the collection
me option from the database
and then there are filters
now filtering means only
selecting the parameters
or the fields with which
you want to represent
or visualize your data
with and there are three types
of filters or you can say
there are three different ways
to limit the data
that is going to be
displayed on your graph.
So the first one our SQL custom
filters the second one are
context filters and third one.
Our traditional filters.
So what is a custom SQL filter?
So a custom SQL filter
is something like
where there is a where clause
and it is placed in the SQL
that queries the data
to be used in the workbook
because behind the tablet
dashboard on which we
are working with there
is an SQL query going on
when you select a filter
so it will specify a
where clause in that
so filter is a tableau term
and it technically applies
only to Next enter
additional filters,
but however,
the custom SQL filter
emulates the behavior
of a global context filter.
So we will refer
to it as such so now
since I was already talking
about context filter,
what is the context filter?
It is filter in Tableau
that affects the data
that is transferred
to each individual worksheet.
So context filters are great
when you want to limit
the data seen by the worksheet.
So when a worksheet
queries the datasource,
it creates a
temporary flat table
and it is used
to compute the the chart
this temporary table includes
all the values are not filtered
out by either the custom SQL
or the context filter.
So just when the custom SQL
filters your goal is to make
this temporary table
as small as possible.
So now let me tell you
about the traditional filters
and tradition filter is exactly
what most people think of
when they think of filters.
So when a tableau is creating
the visualization it
will check to see
if a value is filtered out by
traditional filter and it is
not Dad the table level
and it is very slow
among all the filters.
But let me tell you
that it does have an advantage
because creating a traditional
filter is very easy.
You just simply have
to drag a field and drop it
onto the filter shelf.
So I'll be showing
you all in the demo
where to drag your parameters
or different fields
from and where to drop it.
So all this filtering
that we are doing it is
for enhancing The View
that we are going to make
and there are other ways
for enhancing the view to
the next one is representing it.
With hierarchies now
in order to enhance
your data you can use sorting
to but sorting may not always
be the right option
for representing your data.
So while there are
other methods to also
enhance your data sorting
can be one option
but sorting cannot always
be the right choice
when you want to represent it.
So you also have
to be able to drill down
to granularity of your detail
to and hierarchies
can provide a way to do that.
So you can start with
and high-level overview of data
and then drill down to Levels
of detail on demand.
It means that you can represent
even the granular form
of data by hierarchies.
So if you see
in the diagram over here,
so there will be a category.
So this is going to be
the high level overview
and there would be subcategories
about your data to for example,
maybe you are representing
your data by countries
and you want to go down
to granular level
and you can Define it by let's
say states in the country
and then you can drill
down even more to cities
and then to street
names in the city.
He's like that so hierarchies
are very important as well.
So that other way
of doing it is grouping
the grouping signifies
the all the related fields
that you can use
to represent something.
So for an example,
let me give you the example
of representing the marks card
of a particular student now
if it's a high school,
you know that all
the subjects differ
in each of the standards.
So what you can do is
that you can group
similar subjects for eight.
Then you can group
the different subjects
for nine standard together
and thereby 10 standard also,
so you can filter
out the field names
of the particular subjects
and you can group them
as class nine subjects
and you can group them as
standard 8 subject
standard nine subjects
in standard 10 subjects.
So grouping also
allows you to organize
and manage your Fields as well
and next our sets
so Setzer nothing
but there are a collection
of Dimension members.
So dimensions in Tableau
are used to add level of detail
onto your data and set
is a collection of
different dimensions together.
So I'll be telling
you what dimensions
and measures are
so you'll understand sets
in an even better way
when I tell you
what dimensions are.
So for now,
you can just understand
sets are nothing but a group
of Dimensions which are nothing
but different fields now,
let us see the different
data types in Tableau.
So the first one is Boolean,
so as you all know what
Boolean is it is either a true
or false value the next one
are Numbers like for example,
you can see like 300 400
starting from zero
to Infinity all
the whole numbers.
Then you can also
represent decimal numbers
and Tableau as well.
Then you have got a data type
which is called date
or date and time stamp.
It means that it will specify
the month day year
and also the time so
this is represented
in this format next.
It can also represent text
or string and this
is one data type,
which is very unique
which is Geographic.
Values so if your feet
ever contains something
like country City,
it will auto
detected the latitude
and longitude measure
and this is auto
generated by Tableau
and it will detect it
as a geographic value.
And this is where you can use
the geographic representation
in the show me panel.
So whenever you have a feel
like City region countries
or anything that is related
to a location you can see
that it will
automatically generate
that visualization for you.
So these are the data types.
You can use in Tableau.
Okay.
Now, let's talk
about measures and dimensions.
So what is the dimension
a dimension is a field
that is an independent variable.
It is used to add more levels
of detail onto your data
and it is usually a text.
So whenever your data source
will detect any kind
of text the field
that is only filled with text.
It will order detectors
as a dimension and
if there are numbers it
will auto detected as measures.
So if sometimes
there are numbers
that you wanted
to represent as a text,
for example, let's say the year,
you don't want to perform
calculations on it.
This is a year and you can treat
it as a dimension or a text
so you can also explicitly
Define which is a dimension
and measure in Tableau.
So dimensions are used
to add level of detail now
measures are just numbers.
So let's consider
the same example again
of a class of students
and their marks.
So their Mark
since they are numbers they
will be treated as measures
and Tensions could be
the name of subjects.
So if you just say a mark
of a particular student
that makes no sense.
But let's say
that you add something
like the mark of a particular
student in science Mark
of a particular student
in social studies marks
of a pretty good
student in literature.
So that will add
levels of granularity
and levels of detail.
So that is what dimensions
can be used for.
So now let us see
how successful Tableau has been
in the past years.
So this is
Gartner's magic quadrant.
Quadrant and this is
the magic quadrant
that Gardner created
for the business intelligence
and analytics platform
and you can see Tableau lies
among the leaders quadrant and
this is the ability to execute.
So this lies on top and let
me tell you the Tableau
has been leading
in Gartner's magic quadrant
since the past three years.
Definitely Tableau is
the winner over here.
Also, let us see
what different companies
who have been using Tableau
has got to say about it.
So there are articles published.
About Delight about
how they're using Tableau.
So there was something
like the Lord builds a culture
of enablement with thousands
of Tableau users.
The same data finality
takes the finale's
scores goal with data inside
and they're all using Tableau.
So you can see
how popular Tableau has been
and still is and I'm pretty sure
that Tableau will stay popular
because you've already seen
what can we do with Tableau.
So now it's time to show you
all the demo of how to use Tablo
and for that we will take
in account the u.s.
Crime data set.
So this data set
contains different incidents
different crime incidents.
So we'll take a look at
what this data set contains
and we'll make analysis
of this data set by making
visualizations in Tableau.
So let's get started.
So this is my Tableau desktop
this the version is 10.2.
So the first thing
that I need to do,
I need to connect to my database
and here you can see
all the options.
Are all the kind of data sets
that you can go connect
to you can connect it
to any kind of server.
You can connect it
to a local file
that is in your system.
So my file is a CSV file.
So I'm going to go ahead
and click on more
and this is my file.
So go select update now
and I have chosen
a life connection.
So these are the different
fields in my data set.
So I've got a record ID
the agency gold agency name type
the city the state
year month incident.
So these are all
the different fields
that we are going to work with.
So it contains the crime type
whether the crime is solved
or not the victim sex
the victim raised victim
ethnicity the perpetrator,
which is basically the killer
that's a fancy name
for a killer so This is all
the killer details the weapon
and it contains
the crime records on u.s.
Across multiple years.
So we'll go ahead
and we'll play around
with this data set.
So next thing you need to do
you need to make a worksheet
where you can create
all those visualizations
in so here just click on this
or click on here to go
to your worksheet.
So just click on here to go
to your worksheet.
So this is your first sheet.
And the first thing
that I want to find out
from this data set
is which state has
the maximum number of victims
or which state has
the maximum number of crimes
and that'll can find out
by taking an account
the victim gone.
We can see
that how many Who were killed
so the place or the stage
where maximum people
were killed should be
the highest crime state.
So this is
what we're going to find out
and these are dimensions all
the text fields and hierarchies.
So we've got the measures here
which are numbers like victim
count age perpetrator count
and age and incident.
So now I want
a geographical visualization.
So for that I have got
my geographical measures.
So these are the local values
that will help me
to plot the visualisations
on a world map.
So here I'll just drag and drop
longitude on two columns
and latitude on two rows
and then let me select
state and put it on
to detail and let me just take
victim count and put it
on color and I can see
that the colors
are different some
where the blue is darker
and the place
where the blue is
the darkest is California
and the victim count is 50 here
next one might be Texas
with seven thousand
and forty eight victims.
Now, this shows the overall
victim count across all years.
And if you want to see it just
for a particular year so
we can add filters for that
because we have God Dimension,
which is year.
So you just drag
and drop here on to filter
and you can select
if you want it
for a range of values
or at least at most so
you can just drag it.
It and see the view
across 10 years
or 5 years old together.
So just click on OK
and then just click
on show filter.
So this is going to be
something like that.
So I want in from 2000 to 2014.
So still California has got
the highest victim count
with 4002 and let's say
that you won't descri
that is you want it to see
for one particular year
then just go ahead.
Select any year.
Let's say 1980 click
on OK click on show filter.
So now you've just selected
1980 now so New York
has the highest number
of victims in 1980
with a count of four hundred
and twenty six and then
comes California with 322.
Then I guess Texas with 230
and Florida with 148.
So this is how you
can use different filters
and we found out
that California has
the highest number of victims.
So the crime rate is
really high in California.
So now we're going to find out
that what are the weapons
that are used most
in order to kill people.
So we'll find out
the favorite weapon
of the Killer's so we'll just
make another worksheet.
So now I'm going
to do the same thing.
So I'm going to put
longitude and latitude.
In columns and rows,
it's not again.
I'm going to select victim count
on two colors
and then in detail,
I'm going to put City.
So now what I'm going to do,
I'm going to change the view.
Okay.
So since this won't give
me any kind of picture even
if I select weapon over here.
So I think I should go ahead
and select different views
in order to understand
or identify that which
where the objects are
which were the weapons
that are used the most So now
I'll just go ahead and select
the bubbles packed bubbles.
So this is what you see now,
let us take in weapons and put
it on two colors.
So now you can see
that all these colors
which is like a teal blue.
I'm not good with colors
but this blue over here,
which is quite different
from this blue.
So this blue occupies a lot
of color and this is handgun
and this blue represents.
And again and in
California it see
that the victim count
in California with
handgun is almost 6566.
So out of all the eleven
thousand people were killed 6566
were killed by the handgun
and apart from that.
A lot of people are
killed by knives also.
So this blue over here this
represents knife the pink one
over here is unknown,
which is the light pink
and the Deep thing
which is a Deeper color
of pink this represents rifle.
So the red here is Poison
the purple one represents
Suffocation the blackish purple
one represents a shotgun.
So the green one is fire
and this green one is a firearm.
So even if it doesn't give
you a clearer picture,
you can go ahead
and change the views.
You can also try
a tree map over here.
So this is another inside
that we made so
now let us find out
that which City has
the most amount of perpetrators.
Now.
This is going to be
very similar to the one
that we find out
where it has the maximum
number of victims.
So instead of victim count
will just go ahead
and select perpetrate account.
So we'll do
the same thing again.
So again, it is showing
that California has the maximum
number of perpetrators,
but we want to find out that
whether the perpetrator count
or the victim count
are directly correlated or not.
The one to find out
that in a state
where there are more victims are
there more Killers or not,
or is it something
like there is one person
who is going around
and killing everybody
so we'll find that out.
So in order to do that,
so let's go and create
another worksheet.
So here let me just find
out State and put it on.
Columns and enroll,
I want perpetrator count
and I also want the victim count
so you can see that there is
a direct correlation,
but if you want to observe
it properly so you can just
go ahead and do this.
You can represent
it in a dual axis.
So now you can see
that the orange ones
represent victim count
and the blue ones represents
perpetrator count and let
us go ahead and change it.
Let me just put a Line,
so now I can see
that the blue ones which
represents the perpetrator count
and the orange portions
represent the victim count
so you can see that there is
a direct correlation.
So it means
that the murder scenario
something like 1 to 1.
So 1 Killer goes
and kills one person.
So there isn't much of a hint
for someone to be
a serial killer.
So this is one more inside
that you can make
from this visualization.
And finally you can go ahead
and create dashboards
if you want to Resent
it to someone.
So these are the worksheets.
You can also go ahead
and rename this worksheets.
So here it is the reading sheet.
So this is the let me call
it the highest victim so
similarly you can go ahead
and rename everything.
So now if you want to include it
in your dashboard,
all you have to do is just drag
and drop your sheets over here.
So this was the one
with the filters
that we created.
The filters will also be
here then let me just drag
and drop sheet number two.
You can also adjust the sizes.
Let me close this data pane now.
There's might be
some space here also, yes.
Oh so you can also zoom in
and zoom out it
with the dashboard over here.
So if you want to present it
to someone you can show
all your work sheets at once.
So this is what you
can do with Tableau.
Tableau is known to create
interactive visualizations
that are customized
for the Target demographic
and what better way to learn it
than a step-by-step tutorial?
Hi all this is a pasta
from Eddie Rekha.
And today we're going to talk
about charts and Tableau,
but first let me show
you the topics.
I'm going to cover for today.
First of all,
we're going to talk
about the generated fields
in Tableau followed
by the used cases
of those then we're going
to talk a little bit about
building charts and Tableau,
which is the major focus
of this session.
Then we're going to talk
about the pros and cons of tab.
And finally, we're going
to conclude our session.
So without Much Ado,
let's get straight
into the module.
Now Tableau generate
some fields,
which can be visible
in the data pane.
Now these fields are generated
in addition to the fields
that are present
in the data set.
Now, the generated fields
are measure names measure
values the number
of records and latitude
and longitude now measure names
and measure values
are two Fields created
in Tableau by default.
Now, these fields are created
when a Data set
is imported into Tableau.
So you can go into the data pane
of the worksheet
and view the fields
as I'm going to show
you in a little
while a measure name consists
of all names of a measure
present in a data set
and is always present
at the end of the dimension.
Whereas all the measure values
present in a data set are kept
together in the field
called measure values
and it is also always present
at the end of the measures
list it consists
of all continuous values
of all measures
and we talked about number
of God's for those of you
who have worked
with Excel sheets
and power bi before.
Your number of Records is
basically like a count variable.
It shows the count of Records
present in a data set.
It is also an auto-generated
field in Tableau,
which assigns the value
of one for each record present
in the data set.
It can be used to verify
the count of Records
when joining multiple tables
as well apart from that we
have the latitude and longitude
which are basically baited with
geographical detailed present
in a data a data set should
consist of geographical details
like City country
or state for this particular
generated field to be used.
All right.
So, let's see
how we can use them.
Now.
I'm going to be opening
a new sheet in tableau.
Alright, so, let's see
how measure names
and measure values work first.
So I'm going to pick up
the highlighter to show you
where you can find these so
here I have my highlighter.
So here at the dimensions shelf.
You can find the measure names
and here are the measures shelf.
You can find the measure values.
All right.
Now in the first case we're
going to be using measure names
and measure values
which can be used to see
the aggregation of all men.
Was present in a data set.
Now these fields can be shown as
different types of visualization
in Tableau Caswell.
So what we're going to do
first is we're going to drag
the measure names
into the columns
and drag the measure values
into the rows.
All right.
Now if we turn the marks shelf
into automatic Tableau
automatically gives us
a bar chart and if not,
you can go into the marks card
and select a bar chart now.
This visual is created
for all measures
present in the data set.
And as you can see,
we have discount number
of Records profit profit
ratio quantity and sales.
Same thing.
We can see here.
All right here we can see
all the measure names
and measure values moving on.
Now you can do a bunch of things
with this particular measure,
for example,
if you suppose want
to delete a measure value,
you have the option right here.
I don't want to delete any
and also you can create
an alias for measure names.
It can be shown
in the visualization for better.
Station so we go
to measure names.
There's an option
known as edit Alias.
I'm going to select that.
And in this example,
I am going to give
the quantity volume sales.
And then click on OK and
as you can see the name
has changed right
here in your graph.
And these are just
a few basic things
that you can do with this
if you want to analyze
multiple measures
in a single visual this can also
be done using measure names
and measure values.
All right with that.
Let's go to our other
generated Fields.
I'm going to create a new sheet
for this new worksheet
and we're going to talk
about the number of Records.
So again for this I'm going
To drag the number of records
from the measure spin up 2 rows
and this basically gives
us the number of Records
which is nine nine
nine four pretty basic.
There's nothing much you
can do about it.
But when we are going to
aggregate on the bigger numbers
in the bigger data set,
this is something
which will be very useful to us.
All right,
let's add another sheet
and we're going to see
how we can use the latitude
and the longitude now
as I had mentioned
before these fields
are associated with geographical
detailed present in the data.
So you should have something
like a city country
or state in your data set
so that you can use them now
unlike other bi tools
like power bi where sometimes
you have to mention
that a latitude or longitude is
in fact geographical data here.
Tableau is smart enough
to Auto generate these measures.
So I'm going to take
the latitude here
and the longitude here, okay.
Let's just switch it up.
Let's put the latitude here
and the longitude here
and you can already see a map
appearing a second step
will be to drag the state
from the dimensions and put it
on this detail present
in the marks card list
and this creates
a geo-mapping visual
as you can see
on your screens right now.
You do not have to select any
visualizations just by dragging
and dropping your latitude
and YouTube's Tableau
smart enough to understand
that it has to create a map.
Now that was all about
generated Fields with that.
Let's move on to understanding
how and when to build
different types of visuals.
Now Tableau is known
to create interactive visuals
for easy data interpretation.
So you can create various types
of graphs and Tableau based
on the purpose now
the different charts
that can be
created using Tableau
and their specific purposes.
Something that I'm going to show
in the next segment
of this session.
So we're going to start
with the bar chart.
Let's go back
to our Tableau desktop
create a new sheet for this
and named it bar
chart pretty basic.
Now.
This is one of the
very basic charts.
All you have to do is
take something on your x
axis and take something
on your y-axis
and by default it
is going to be made
into a bar chart using Tableau,
so I'm going to take category
of product in my columns
and I'm going to take
see the profit into the rose.
And as you can see
the automatic feature
will turn it into a bar graph.
And if it doesn't you
can just go to the marks card
and select bar graph.
Alright, the next basic chart
we're going to see is
the line chart again.
I'm going to name this sheet.
It's always good to be organized
because in the end we
are going to use bi tool.
For organization and
analytics, right?
So here's my line chart.
So line chart basically
is used to compare the data
over different periods.
A line chart is created by
basically connecting a series
of dots now these dots
represent the measured value
in each specific period.
So again, this is pretty simple.
I'm going to take the order date
as I just mentioned it shows
data for a fixed period of time.
So I'm going to take
the order date in the columns
and And I'll be taking sales
this time as my measure
and it automatically
creates a line graph.
Now Tableau is smart enough
to figure out what kind
of graph would you need
for certain data,
but even if it does not you can
always go into the marks card
and select the kind
of graph you want next is a kind
of complicated graph,
which is the Pareto chart.
This is basically a combination
of both the charts
that I just showed.
So Pareto chart
consists of both bar
and line graph the same measure
is used to create the graphs.
But the measure values
are manipulated differently.
Now, the purpose
of using a Pareto chart
in Tableau is to identify
the contribution of members
that are present
in a particular field.
For example,
the prophet contributed
by different subcategories
of a product in a retail store
can be analyzed
using a Pareto chart.
It can be used to show
the Top members
and their contribution as well.
So let's try doing that.
I am going to be taking
the sub category
of products putting
it in my columns.
Then I'm going to take
profit and put in
my Rose now stay with me
because this is kind
of a longer process
than the other graphs,
but it's pretty useful.
I assure you I'm going
to right click the sub category
and select the sort option.
It will going to open
this sort of a window.
I'm going to select
the descending order
and then And I'm going to go
to the fields and my feel name
is profit aggregation some
okay, then I am going to drag
the prophet measure
again into the rose.
It's going to create
two separate graphs like this.
But if I right click here,
you can see an option
called dual axis.
I'm going to select this
and it's going to turn
this into circles.
It's basically merged the x-axis
of both the measures
and has Loaded it
into the visualization
that you can see right now.
Now next you have to go
to the marks card
and select some profit
and as a drop-down
appears going to select bar here
and I'm going to go
for the color a lighter blue.
Okay.
Now I'm going
to the other prophet
and I'm going to select
the line graph here.
And I'm going to go
for the color orange.
Okay, maybe a darker orange.
Alright, this looks better.
Now I'm going to select
the sum of profit
and second one right here.
And I'm going to right
click here and choose add
table calculation from the list.
Now.
It opens this primary
calculation type of a window.
I'm going to select running
total from the calculation type
because that's what we want.
Right.
We want a running total
and then select some
as the aggregation
which has already selected
and compute using table across.
All right.
Now I'm going to add
a secondary calculation
and this is for our second graph
and here I'm going
to select percent of total.
All right table across
as we have done before now.
I'm going to be
closing this window
as you can see the line graph
has changed and it's not on top
of the bar graph anymore.
That is because we
have separate primary
and secondary calculations.
The line graph here is showing
us the total running.
Some of profit
as we had calculated
and as you can see here
and now you can basically select
and change colors
that you want to make the graph.
Look as you like.
I'm going to keep it as it is
and this is the procedure
to create a Pareto chart
in Tableau next in our list.
We have a bullet shot.
I'm going to rename it.
No bullet shot can
be used as a gauge
or an indicator to show
the performance of measures now
to measures can be compared
to each other using
the bullet shot.
For example,
if you are having
to estimate say actual profit
versus estimated profit,
we can compare both
of them using the bullet shot.
Now.
This is going to be slightly
different from the three charts
that I showed before here.
We're going to start
with the analysis option
present in the menu bar.
All right, select
create calculated Fields.
It opens this sort
of A field window
and going to just name
it as estimated profit.
We're going to type
an estimated value
in this example.
The profit is taken
as the measures.
So the calculated field
is created for estimated profit.
So I'm just going
to type in a number.
Let's just keep a 300,000.
Now.
The good part about Tableau is
that till your
expression is valid?
It is not going to let you apply
the changes you have made
in a calculated field,
which is great for beginners
because then you
will know exactly
where you have gone wrong.
For example,
if I remove this you
can see the field shows
that the calculation
contains errors.
Not just that it
will even show you.
The syntax of what your
expression should be so here
when I type a number it shows
that my calculation is valid
and I can apply this
and there you are now go
to the measures in the data Pane
and you have to hold
the control key on the keyboard
because you have to select
two different measures.
So estimated profit
and profit now click
on this option called show me
which will show you
the various graphs
that you can apply.
Why here top right corner?
This is the option is
the option I'm talking
about and you can see
the bullet chart option
also being highlighted
which means we can use
this particular option
for the measures
that we have input.
So I'm going to select this
and you have your bullet shot
next on our list.
We have text tables.
So let's just add another sheet.
Going to a new worksheet
to do the same thing.
So a lot of this is going
to be dragging dimensions
and measures and dropping them
into columns and rows.
Don't mind me not repeating
it again and again,
So after we've gotten
a table like this,
I'm going to drag this profit
into the text box
present at the marks card.
And here you have it.
It creates a text table
by default next up.
We have a heat map.
Now, this is basically a graph
which can visualize
the data in the form
of size as well as colors
on different measures.
Now two different measures
can be visualized simultaneously
using a heat map.
So one measure can
be assigned to size.
Whereas another measure
can be assigned to the color
of the Heat map.
So let's go ahead
and create one.
Now again, I'm going
to be holding the Ctrl key
on the keyboard
and select subcategory
and sales from the data pin.
So let me just select these two.
I'm going to go back
to the show me button
on the top right corner
of the worksheet
and select the heat map
and it's going to look
something like this
does not have any color now.
I'm going to take
this profit measure and drag
and drop it in the color.
Now I'm going to drag
let's say region.
Where did the region go?
You're all right.
I'm going to drag the region
and put it in the columns.
And now this has
created a heat map
which can be used
to visualize sales and profit
across different dimensions
in different regions.
Next.
I'm going to show you
how to make a waterfall chart
which is also one
of those charts
which requires a little
more work than the others.
So, let's see
how it's made my God.
We've got like 13 Sheets, right?
Let's rename this.
All right now waterfall
chart is a chart
that visualizes the cumulative
effect of a measure
over a dimension.
It basically shows
the contribution of growth
or decline by each member
of a dimension.
Now, let's take for example,
you can see the contribution
of profit by each subcategory
by using a waterfall chart.
All right, so we'll start
by making a basic bar chart.
So we go to a new worksheet
what we had done
for the bar charts thing.
Take the subcategory put it on
the columns take profit and put
in the Rose by default.
It creates a bar chart
as I had mentioned
earlier in this session.
Now, I'm going to right-click
on the prophet present
in the measure spin.
I'm going to choose
create and then go
to the calculated field option
which opens up
a window like this.
Now you're going to take this
and do exactly what I'm doing.
So I'm going to name
this negative of profit.
And I'm going to put
a negative sign right here.
I'm going to apply
this now we are going
to use this newly created
calculated field negative
of profit into the size option
present in the marks card.
It's just drag and drop it.
So it shall give you a graph
like this after which you
need to click on this some
of profit present in the rows
and select quick
table calculation
and take a running total option.
Now, the reason why we
are taking this negative ad
hoc calculation is to fill
in the gaps in our bars
when we are going to turn it
into a Gantt chart.
So basically we're going to turn
it into a Gantt chart right now
in the marks card,
and now this will create
a waterfall chart
as you can see on your screens.
Now talking about
the Gantt chart.
Let's see how we can create
a separate Gantt chart.
Now again shot is the one
which shows a comparison between
data in different categories.
So it's basically used
to identify the time taken
for each process.
So let's try to make one now.
We're going to take
the drop-down button
in the marks card
and select Gant bar
from the list now.
We're going to drag order date
and put it in our columns
and then Right click on it
and select day now.
Let's click on analysis
and the menu bar
and create calculated field
like we had done earlier
in the session.
I'm sure all of you
might be familiar
with this window right here.
You can type time for shipment
and we're going
to use this formula
called Date difference.
Now as most of you
might have noticed before shows
the syntax right here.
We're going to put in
a date part a start date
and end date and start the week.
So I'm going to put
the date part as day
in single quotes comma
in square brackets order date,
which is a dimension.
So it appears
automatically next.
I'm going to have ship date
now the last start
of the week you may
or may not put
because now that is
an optional part of the syntax.
So I'm going to choose
to not put it and I'm going
to apply it as is now
I'm going to drag this time
for shipment into the size part
and I'm going to take
ship mode and put it up in rows.
And now this is
created again shot.
It shows the time taken
for each shipment
across different ship modes.
All right.
Now, let's go back to something
a little more old-school
presenting the pie chart.
Now a pie chart or something
as most of you might be knowing
it basically shows
segment wise data.
It can show the contribution
of measure over different
members in a dimension
and the angle of the pie
determines the measured value
and basically it's one
of the most colorful charts
different colors can be assigned
to the pie to represent
different members
in a dimension.
Now we're going to do this
on a fresh worksheet not going
to spend too much time here.
We're going to just select
segment and say Tales
from the data pin
and then go to the show me
button and select the python
and there you have
it pretty simple.
Let's move onto
another very useful chart,
which is a scatter plot.
Now the relationship
between two measures
can be visualized using
this particular plot.
A scatter plot is designed
by adding measures in both X
and Y axes this
basically shows the trend
or the relationship
between the measures
that you select will be going
to try doing that.
We're going to drag
discount into columns.
Here's the discount put it
in the columns will take
sales and put it in the Rose.
Now this basically creates
a scatter plot by default
as you can see now.
I'll be taking the subcategory
into the color icon
and dropping it right there.
Now.
This basically has created
a scatter plot showing
the relationship between
the discount and the sales
for each subcategory
as you can see multiple
bubbles with that.
Let's move on to the area chart
now an error chart can represent
any quantitative data
over a different period of time.
It's basically like a line.
Off where the area
between the line and the axis
is generally filled with color.
Now we're going
to hold the Ctrl key
in the keyboard and select
order date and quantity.
Next we're gonna click
on the show me bar right here
and select the area chart icon.
Now, we're going
to drag the region
from the dimensions pane.
So that can add it
in the color icon here
in the area Tab and this
creates an area chart as you
can see pretty simple with that.
Let's move onto another very
basic chart called
a dual axis chart.
It's basically a chart
which can be used to visualize
two different measures.
In two different chart
types a date column
and two measures are kind of
a basic necessity to build
a dual access chart
the different scales
in this chart help the user
to understand both measures.
So again, I'm going to hold
onto the control key and select
order date sales and quantity.
So odd date sales and quantity
in the show me tab.
I'm going to select
the Dual combination.
Ocean and this creates
a dual axis chart
as you can see
it's pretty simple.
Now you can change the color
do anything you want
with this chart me personally,
I would like to keep it
as is I think blue
and orange create
a very good contrast
which makes your data visible
and clear next and
the penultimate chart
I'm going to talk about is
the bubble chart now above
chart visualizes the measures
and dimensions in
the form of bubbles.
It's kind of like
the scatter plot,
but it contributes
to more effective visualization
It's as simple as that.
All I have to do is click
on the packed bubbles option
and it has created
a bubble chart
as you can see and finally we
have a very important chart
but also a very basic chart
which is a histogram
now a histogram shows
the values present in a measure
and it's frequency it basically
shows the distribution
of numerical data
as it shows both frequency
and measure value by default.
It can be used in many cases.
For example,
if you want to analyze The
discount given by a retail shop.
You can visualize
the amount of discount
and it's frequency
using a histogram.
So we're going to go to a new
worksheet select discount
from the measures
and click on show me
and this is the histogram option
and this is our histogram.
It was as simple as that would
that I've come to the end
of all the charts
that I had to show.
Let's move on
to the features of tableau.
Whoa now as discussed
before Tableau is one
of the most comprehensive
business intelligence tools
in the market right now
since its Inception,
it has already witnessed
a steady growth and has gained
a wide market share in the bi
and analytics space.
So it's clearly one
of the top choices
in the bi space.
So let's talk
about a few features,
which has made it gained
its wide market share in bi
and analytics space.
So first of all,
it's amazing day.
Visualization Tableau bi is
known to offer the most advanced
data visualization options
and is definitely
a market leader.
The users can easily perform
complex data visualizations
by using the drag
and drop feature
and it has a slick interface
which is both intuitive
and fast for creating
customized visualization.
It is easy
for any business user to create
customized dashboards using
the complex data and sources
which makes Tableau
a preferred choice
for business users we have Have
quality customer support
and since Tableau is
a fast growing company with very
high customer retention ratio.
Most of Tableau bi users
are satisfied with the product
and the technical support
provided according to a survey
conducted by Gartner.
Tableau is ranked amongst
the best bi tool with respect
to customer satisfaction next up
and very important.
It is very easy
to implement Tableau.
The rich in features
is easy to deploy
and upgrade as per survey
conducted more than 90%
of Tableau users have
the latest version installed
and running it clearly
indicates the ease of use
and upgrade next.
We have data source integration
now Tableau offers
a simple out-of-the-box solution
for integrating with the most
popular data sources
and analytics languages,
like ironpython.
They also constantly adding
support for new data sources as
and when the need emerges
it also supports Hadoop
and Google bigquery API
for robust data analytics.
X next let's talk a little bit
about its excellent
Mobile support Tableau
has clearly understood
the requirements of mobile users
and has developed
a robust mobile app,
which has a very
rich user interface.
It is challenging task
to Showcase complex graphs
and visualizations
on a small Mobile screen,
but Tableau has mastered
this art and the visualizations
adjust itself based
on the screen size of the device
which is being used and finally,
let's talk about
the rich online.
This and Community now
Tableau has got an active
and engaging user Community
which will help the fellow users
to learn and master Tableau.
Now the community is so huge
and is so always buzzing
with ideas and solutions
that it has a vast vendor base
who also offer installation
and customization Services
concluding I would like to say
that Tableau is a bi tool
which is changing
the way we think
about data it helps you harness
the power of your R data
and unleash the potential
of it so does definitely one of
the best bi platforms to choose.
Hey everyone.
This is ratio from Eddie Rica
and welcome to the
Tableau dashboard tutorial.
So by now I have given
you a brief overview
of whatever options
will be exploring in town Loop
and we have covered
mostly all of the options.
Now, let us take a use case
of the Indian Premier League
the IPL in order
to understand Tableau
and depth now for those of you
who do not not know what
Indian Premier League is.
Well, it is a very
popular T20 cricket match
and cricket stars
from different countries.
Take part in the
Indian Premier League.
Now, there are different teams
that play in the
Indian Premier League.
So these are the teams there is
the Royal Challengers of
Bangalore Kolkata Knight Riders.
Good drought Lions Delhi
Daredevils the Hyderabad
Sunrisers rising to
New Super Joe.
Kings XI Punjab
and Mumbai Indians and in India.
This is the most awaited
and the most popular
Cricket tournament ever.
So we'll find out
at the end of this tutorial
that whose team is the best
because we're going
to make analysis
about the IPL teams only.
So let's see whose team is
the best So let's
understand the insights
that we're going to make
using a flow diagram.
Now there is a huge
amount of IPL data.
Now, you know
that every time a boulder throws
a ball and the batsman
that's it and scores a run.
Everything is recorded.
There is a record
of each of the bowl
that was bold each of the run
that was scored by
a batsman don't so you
can imagine the huge amount
of data that we're dealing
with And not just IPL.
You must have noticed
that whenever there is
a match telecasted on TV.
There is a pre-show
or a post show
that is usually held
where the different
experts it together
and they make an analysis
of maybe who's going to win.
They try to predict
who's going to win
or who's going to lose
and how much runs are
they're going to score
or what is going
to be the outcome
of a particular match now,
I'm not an expert in Criminal.
Ticket but definitely
I can ask Tableau to help me
to make all those analysis.
So we're going to do
the same thing.
And even if you have ever
watched those forth
and buyer shows,
you can also see
that sometimes they show
different kind of visualizations
like the Wagon Wheel,
which is very popular with shows
trajectory of every bowl
by a particular batsman.
So they see those visualizations
and they make
an analysis of it.
So we're going to The same thing
with Tableau only we're
not Cricket experts
but we sure are Tableau experts
and we can do the same
thing with Tableau.
So we're going to feed
that huge amount
of data into Tableau
and then we're going
to make different insights.
So the first thing we'll do
is we'll find the orange cap
and the purple cup holders
throughout all the seasons.
So, you know that orange
cap holder is the one
who scores the maximum number of
of runs in a particular season
and a purple cap
is awarded to the person
who gets the most
number of wickets
and that particular season.
The next inside
we're going to make is
who was the man of the match
for a particular match and
who was the man of the series
across all seasons.
We're also going to find
the overall top five players
who have performed the best
throughout all the seasons.
Next we'll find
out the best teams
across the seasons will find out
which was the team
that has performed the best
and we'll be getting
onto this insights by creating
visualizations for each of them
and then we'll publish
this insights onto the website
so you can see this is what the
official IPL website looks like.
It has got the
batting leaders names
and what is the score card
for each of the team?
Now, let's have a look
at the data set
that will deal with so
the first data set
that we have is the team table.
So we have a team ID
and for every team ID there
is a corresponding team name.
So the team idea
of Kolkata Knight Riders is one
for Royal Challengers Bangalore.
It's 2 and so on and there
is also a team short code
to identify each of the teams.
So for Kolkata Knight
Riders, it's KKR.
Similarly RCB
Chennai Super Kings.
RCS K and the opponent ID
and the opponent name are
basically the same thing
that we have just gone
through the team ID is the same.
The opponent ID
is the same field
as Steam ID say it
has got the same team IDs
and the opponent ID has also got
the same numbers and similarly.
The opponent name is similar
to the field team name
and opponent short code is
also similar to the field
which is team short code now.
I'll be telling
you why do we need?
Need the data set to be
like this next up.
We have the player table.
This is just an example.
Now.
This is just a part
of the data sets
and there are a lot
of rows in our data set.
So I cannot incorporate
that in this screen.
So I just mentioned
24 names over here.
So we've got the player ID
starting from 1 and we have got
player named corresponding
to each of the player ID.
We've got the date of birth
of the particular player.
If is a batsman
which is Batting hand
if is a bowler,
which is his bowling arm
and the speed of the bowler
also the country where each
of the players belong to and
if he was an Umpire.
So if it's zero,
it means that he's not next.
We have got the match table.
Now this contains
all match details.
So I've got a match ID.
We've got matched date the date
where the particular match
was held the team ID
and the opponent ID are
the ideas of the teams
that have played on that match.
The season ID is which season
of IPL was this match held
on the venue the stadium
where it was held
which was the team
that won the toss
and what did they choose
after winning the toss then
if there was a super
over if it's zero,
it means that there were
supernovae that means
the match resulted into a tie
and to break that tie they
played some extra hours.
If it's zero,
it means that
though it wasn't Hi,
there was no super
overplayed at if it's one.
It means that was and is result
this field indicates
that if at the end of the game
there was a particular winner.
So if it's one it means
that there was a winner
and that particular match
if it's a Duckworth
Louis now this means
that whether the match
was interrupted due to
rain or something
so that they had to reduce
the number of overs
and hence make a new Target.
So if it's zero,
it means that they'll wasn't
condition like that.
That and then there
is the wind type
whether they won't buy
runs or by wickets.
And how many runs
did they win by or by
how many wickets did it went by
and this is the mouse winner ID
which team ID was the winner
and then the man of the match
I did this is the idea
of the particular player,
which was chosen as
the man of the match.
This is the first time buyer ID
Second Empire the city
where it was played
and the host country
and up next.
We have the player match table.
Now, it contains Fields
like Hid player ID team ID
is keeper and his captain
so you can see
that match ID is similar
and these are the players
who have played
on this particular Match
3 3 5 9 8 7 and these are
the player IDs of the players
who played this match
and there is the team ID
that player number one belongs
to you team number
one Play number 2 belongs
of team number 1 and hence 10
belongs to to an diskeeper.
So this is for
a particular player player.
ER one if he is a wicked keeper,
it will be 1 and if he's
not it's going to be 0
and this is for its Captain.
It is a Boolean again.
It shows that
if the particular player
with the player ID one is
a captain then we'll be one
and if it's not it will be zero
and now we have
the season table.
So now it contains the season
IDs the season a year.
So the first season
has the season ID
1 and it was played at It
the orange cap ID was hundred.
It means that the player
with the player ID hundred
has been given the orange cap
2 and similarly is the same
with purple Gap ID
and the man of the
series IDs over here.
So this highlights
the orange cap purple cap
and the man of the series
for a particular season.
So these are the data sets
that will deal with
and I will get started
with the Practical demo
will perform all this
will use all this data sets
and we'll make the following.
Insights that we talked
about so here it is.
This is my Tableau
desktop and this is
where I'll be creating all
my workbooks and worksheets.
So the first thing
that you need to do is connect
to your data source,
so you have to click on connect.
So the data source
that I'm going to use
are all CSV files
so I don't see it
here in the option.
So I'll just click on more.
I'll browse on to the folder
where I have my data set.
So I want this CSV
file over here,
which is steam.
So I'll just click on here click
on open and so there it is.
And these are the fields that
this particular file contains.
You can see that there are
other data sources are there are
different files over here.
So this is because these
are the other files
that is contained
on the same file location.
So I need a lot
of details right now.
So the analysis.
Is that I'm going to make is
not sufficient with just three
of these feelings.
So I need to incorporate
more data sources.
So in order to do that,
you don't have to do much.
You just have to drag
and drop the other data sources
that you want to
integrate this with.
So the next data source
that I want is
the mash dot CSV here.
So I'll just drag
and drop it over here.
Now.
This will ask you for what kind
of joint that you want
whether you want an inner join.
That means it will have
all the common fields
from The teen dot CSV
and mash dot CSV
whether you want to left join
that we have the common part
of Masher CSV and team dot CSV
and the entire of Team dot CSV.
Similarly.
Write joint is entire
off Mash dot CSV
with the common part
of both of these
and full outer join means it
will contain all the fields
from both this to data set.
So now I have to define a joint.
So I'm going to select
the inner join.
You also have to define
the mapping it means
which of The two fields
that you want to match
with so imagine team ID
from Team dot CSV and a mapping
it to team name ID,
which is in match dot C
is so now there it is.
You can see how the integrated
data set looks like.
This has got all these different
fields over here.
Okay.
Now I want some more
so I need the mash dot CSV again
because I need to map
some other two Fields also.
So again, I'm going to choose.
Inner joint and here
I'm giving team ID
should be equal to opponent ID.
Now I need one more data set
which is player match dot CSV.
I have to Define team idea
of Team dot CSV
should be equal to T Mighty
from player Master CSV.
Okay.
So this is done also now
I'll be making an analysis
during the entire season.
So I'll include this
to season dot CSV.
So when defining our inner join,
so I'm going to match
the season ID from Mash dot CSV
on to the season ID.
season year and I need to map
season ID from season dot CSV
So now I'll just drag and drop
this season dot CSV file.
And I'll match the season ID
from match dot CSV to see
the 90 offseason dot CSV.
So again, I need to integrate
my other CSV file,
which is the player
dot CSV file.
and here I want the player ID
of match dot CSV Here
one the player ID
from Clear Mash dot CSV
should be equal to player ID
in player dot CSV,
which is there
and I'm done integrating my
different data sources together.
So now we'll go to our worksheet
and first let us make
a visualization of all
the orange cap holders.
And as you remember
that an orange cap
holder is the person
who scores the maximum number
of runs in the entire season.
Let us also rename the sheet
as the orange cap sheet.
Okay.
So now what we need to do is
we just need to drag
and drop feels over two rows
and columns and the fields
that needs to be dragged
and dropped all depends
on the kind of visualization
that you want.
Now for orange cap holders.
I need the names of all
the orange cap holders
for all the seasons.
I want to know who was
the orange cap holder a 2009
who was a 10 11 and so on.
So what do you want
in our column section is
the season year now,
we'll find the season here
in the season dot CSV.
So here it is.
So just drag and drop
this field over here.
And I'll drag
and drop the orange cap ID
in the row section.
Okay, so here it is.
So now we have got
the orange cap ID
and the season here
and the ID needs to also tell
me the name of the player.
So we'll create
a calculation field to find
out the name of the players
who actually got the orange cap.
So you go to create
calculated field.
Let us name this calculation
as orange cap calculation
so it will be
if orange cap ID would be equal
to player ID from player dot CSV
then I want the player name.
And make sure you end it
and click on OK at the bottom.
You'll always find
your calculator feels
that you have created.
So what I'll do now,
I'll just drag and drop
this over to the Rose.
In order to remove
the null values over here
and change the measure
for orange cap calculation
and I'll change it to attribute.
Yeah, so the null
values are removed.
Now.
Let us make this look
a little better instead
of a vertical lines over here.
Let us put some little caps
since we are talking
about orange caps.
So you can just go
how you want to represent it.
Go to shapes click on shape
over here go to more shapes.
Go to the folder
where you want to choose
your shapes from now.
Remember that the shape folder
for Tableau is
in your my documents.
You'll have a my
Tableau repository
so you can go inside that folder
and find the folder
where it says shapes
and there will be
other folders inside there
so you can copy that picture
which you want to use and put it
one of the folders over there.
So I put it in kpi.
So here is my orange cap.
Okay, I'll choose this one.
Click.
Ah, no.
Okay, and I can see there are
little orange gaps over here.
I'll go ahead and increase
the size a little bit
because they look
like tiny dots yeah
now they look like caps.
So if you know over
on each of the Gap,
you'll know that 4 2008
the orange cap ID of
the player was 100
and the orange cap holder was
shown Marsh similarly for 2009.
It was Hayden
in 2010 such intent.
GE Chris Gayle
Chris Gayle again,
and then Michael Hussey
then it's Robin the top bar
and Warner and then
we're not gonna lie in 2016.
So here is your visualization
for all the orange cap holders
throughout all the seasons
and similarly now,
we'll create our
proper cup holders.
So we'll rename the sheet
and we'll name it purple cap and
we'll do the same thing again,
wherever we have put orange now
in the same Fields
will put the purple cap.
So you remember the first thing
we did was season year
and columns and then in rows
you need the purple Gap ID.
Like we made a calculated field
for orange cap will make
a calculated field
for the public app also,
so we'll name it as
purple Gap calculation.
And the condition
will also be similar.
So instead of just orange cap ID
will use the purple Gap ID
if it's equal to a player ID
from player dot CSV.
Then we need to display
the player name.
Click on OK.
So here is your calculated field
so drag and drop it
to the Rose section.
So you can you remember
that in order to remove
this null values,
which is the measure we're going
to do the same here.
And again will do the same
with the shapes.
So for shape,
I'll choose a purple cap.
Let me choose this one.
Okay, so this is
the increased size.
So again,
if you hover on you
can see that 4 2008
it was so helped unveil
and now piecing and so on.
So now we'll go ahead
and we'll find out
the man of the series
and the man of the match
throughout all the seasons.
So at first we'll find
out the amount of the CDs
in each of the seasons.
Let's rename the sheet
as Man Of The Seas.
So now what we'll do is we need
to find the man of the series.
So I'm going to represent
this visualization
in a geographical way.
That means I'm going
to choose the world map
because every season IPL is
hosted at different countries.
So why not use a word map?
So now you have auto-generated
latitude and longitude feels
so I'll just drag and drop this
I'll draw longitude
on two columns
and latitude on two rows.
So there it is.
Here is my world map
and I'm going to pin
point out different cities
and different locations
where each of the matches
or each of the seasons
were held I put the man
of the series on details
over here so that
if when you hover
around different places you find
who was the man of the series
for a particular year.
I'll also put filters
so that you can find out
that who was the man of CDs
for a particular season.
So now we also have
to create a calculated field
to find the man of the CDs
because we have the player ID,
but we don't have
the player name yet
and a particular data set.
So again, we'll call it.
man of the series calculation
similarly over here with say
if man of the CDs ID is equal
to player ID Then display
the name of the player.
and then end click
on okay over here.
So in detail,
I'll add the city name so find
that in match dot CSV.
So this is the city
name put on detail.
Yeah, and let us put
the host country as well.
So these are the cities
where the matches were played.
So there were cities in India
in UAE and South Africa.
So these are the three countries
that have hosted IPS
from 2008 to 2016.
So let us know put colors.
So for that I'll take them
out of the Seas calculation
and drag and drop it to colors.
So we also change the measure.
Cindy has hosted
IPL a lot of times.
So in the same cities there
were matches played
over different seasons.
So we'll add some filters
so that we can visualize
it year after year.
So in filters first,
let me put the man
of the series id Sometimes
when you can find
different fields just use
this search over here.
So I want the man
of the series idea.
Okay, so here it is drag
and drop it to filters.
So select all
of them click on okay.
I'll select this class
select all except null
because we don't
want null values.
And let us also put
the season year as a filter
because we need to search
who was the man of the series
for a particular season.
So this is the season year and
let me drag and drop it to here.
Just click on show filter.
So now you can see
that this is
for a particular year
where if you just hover
on the top of a circle,
you can see that the man
of the series was Watson.
So you just have to drag
this filters over here
if you want to see
let's say in 2009.
So in 2009,
it is Adam Gilchrist.
Similarly.
You can go on check for each
of the individual years.
So let's check for 2010.
So again for 2010 it is such
in Tendulkar as the man
of the series.
So here you can just drag
over the years to see
who are the Mount
of the matches you could also
choose to represent it in a more
simpler way not in a world map
if you want to so
for man of the match,
let me just show you
a very simple visualization
because sometimes keeping
it simple is the best
So we'll call it
man of the match.
So here we'll do
a very simple visualization
one just use columns.
So we'll just use
a rose over here.
So what we need is
we need the match ID.
And then we want
the man of the match.
Okay.
So let us also create a man
of the match calculated field
because we have to display
the name of the player.
Okay.
So here I'm getting
updates about the IPL.
So it's a good thing
that I am making analysis
of the IPL details from 2008
to 2016 and the time
when there is IPL
going on in 2017.
So if I could have waited
a few more days to get it over
I could have included
this year to so K.
So let me just close this.
Let's get back to tableau.
So we are supposed to call it
man of the match calc, right?
So again, it's a similar thing.
So we'll say that if okay,
let's not searched by
this man of the match ID.
Will be equal to the player ID
then display the player named
just like how we did it.
So click on ok now we'll drag
and drop this field, too.
So let us also include
the city name.
Get there it is.
So in the text part,
let me include something.
Let me include
the wind type over here
so that this column
would get filled up.
Okay, so the team
which was a winner they
won't buy runs or by Wicked
so you can see it
displayed over here.
So let me remove
the null values.
Okay, so we'll add
some filters also over here.
So just drag and drop this one
at filters to so we'll remove
the null values from here
you can okay.
So now you'll find
that particular match ID
who was the man of the match?
Where was it played
and the winning team did they
win by wickets or by runs?
So this is a very
simple way to represent
your visualization yet.
It gives you a lot
of information as well.
So you can add more filters here
if you want to but you
can leave it like this
if you just want
so this was the workbook
for the orange cap
purple cap man of the series
than man of the match.
So now what we will do
is we want to see
the overall best team
that has performed
the best entire all the seasons.
So for that will
create another workbook
because our data source
would be a little different.
So let me just save this one
So I just name it IPL WW1
and now we'll create
a different workbook
for that just file click on new.
So here I need to connect
to data first.
So click on more I wanted
the teen dot CSV again.
So here it is Dean dot CSV
and I'll integrate this
with the mash dot CSV.
I'll Define an inner join
where my team ID will be equal
to the opponent team ID.
And then I need
the team dot CSV again.
So drag and drop it.
So integrate this
with the mash dot CSV,
so I need team ID.
From match dot CSV.
Just let me search
from Mash dot CSV
and I'll select the team
from Team CSV one.
Okay that there is no again.
I'll choose team dot CSV
and drag and drop it here
or choose an inner joint.
I will again choose
match dot CSV,
so I need the match winner ID.
Should be equal to the team ID.
Okay, so here is
my data source,
so we'll go to the worksheet.
So we'll rename it
as the best team.
So now what we'll do
we'll just again drag
and drop some Fields over here.
So we need the match-winner ID
from Mash dot CSV.
That's here.
We need the team name
from Team CSV to Let's get
the team ID from match dot CSV.
There it is.
Then we need the city name.
And we need the opponent
ID search for the opponent team
it there it is.
So I'll just drag
and drop this over here.
So in colors,
let me put team name.
from Team CSV to so over here
in the column section
first thing I'll do is So
in order to represent
it in a better way,
I'll click here.
in measure cell go
to count distinct.
Then I'll add
a quick table calculation.
Let's make it running total.
Let us modify this more.
So we'll right click this one
and we'll use
specific dimensions.
and restart every match
when i d so we'll close
this and let's make sure
it fits the height.
So after you select
the fit height,
you can see the performance
of all the teams together
and you can see
that Chennai Super Kings has got
the highest peak over here.
So the second might be the RCB
the Royal Challengers
of Bangalore with 41
and the score for cska is 44
and here is the Reston Royals
with a score of 40
and close to Rajasthan Royal
is the Delhi Daredevils at 39.
So You can see all the teams
over here Gujarat Lions Rising
Pune Sunrisers Hyderabad
Kochi tuskers Pune Warriors.
You can see
that the highest peak is
for Chennai Super Kings.
So they have performed the best.
They have one most matches
throughout all the seasons.
So this is
how we have a visualization
of the best team.
So now we'll go ahead
and we'll find the best players
throughout all seasons.
So we'll do that in
a different workbook
as well because the data source
is again going to be different.
So we'll save this one.
so we'll call it call it IPL W-2
I'll go to file click on new.
So this is my new workbook.
The first thing I need to do
connect to data go to more now.
I have a data set
known as Ball by ball.
So I'll select
this data set over here.
And I'll integrate
more data sets with it.
So the first one
will be matched or CSV.
Now here, I'm matching match ID
of ball by ball to match ID
of measure CSV, it's fine.
Then I need player dot
CSV drag and drop it.
I'll put the striker idea
over here and I'll map it
to the player ID.
Okay, that's done now.
I need the player match dot CSV.
No, I hear I want the match ID
should be equal to the match ID
in the player MedStar CSV.
Now it automatically
got mapped now next.
I want team dot CSV drag
and drop this and hear
what I need to do is I need
to select a field from Ball
by ball CSV and I'll take
the team batting ID.
And here I'll map it
to team ID from dot CSV.
Okay, so here is my data source,
so we'll go to our worksheet
and we'll rename this sheet
and we'll say top players.
Okay.
So again, I'm going
to go ahead and drag
and drop the different fields.
So the first thing I
need is the player name.
So search player name over here.
Here it is from Pluto CSV.
So here is my player name now.
I want this Striker ID.
from bulb eyeball CSV
I'll select the team ID
then I need the team name.
the season ID
and my chai tea Okay,
so we'll add some more filters.
So we'll drag and drop
this player name.
Over here we'll select
all will click on okay.
We'll also put this in colors.
So now what we'll do we'll
select the batsman scored
into the column section.
Okay, so we'll do
a running total lat
quick table calculations
and running total.
So now what we want is
we want the top batsman's
the top five bathrooms.
Let's say so we'll add
a season filter to it that
for a particular season
who were the top batsman.
So so we'll create
a calculated field.
So Let me call it.
top five calc and this is it.
Let us first choose season 8.
That means last year.
Let us choose season 8,
this is for 2015 season.
So we'll click on OK over here.
So we'll go there
right-click will go
to edit table calculations.
Let's do the same
thing over here.
All right.
So we'll also add
the season ID filter over here.
So let us find season ID.
Okay, since we added
the calculation for the season
8 so will unselect everything.
And let me sort this one.
So now we'll just list
out the top five players.
So we'll go here
at it filter great
on top by field.
Let's select top five.
And we have made
that calculation which is known
as top five calc, right?
Here it is.
And click on okay.
Okay.
So now if you click
on fit height you can see
that these are the top players
in 2015 a be de Villiers.
Rohani Warner Simmons and Koli
so you can hover over here
and if you move your cursor,
you can see this track
the running sum
of the batsman was 562.
For coolly, it's 505.
That's 544 Simmons.
This also 5:14 for Warner
and it's 532 for de Villiers.
So these five were
the top players
according to our visualizations.
We made we use filters
to find out this top
five players in 2015.
So let's go to the actual
website and verify
if these are all correct.
So go to my browser over here.
This is the official website.
Okay, so days are
the 2015 batting leaders?
So we'll view the full list.
And you can see
that we have found our top one.
So we had Warner with 562.
If you remember
that then we had Simmons
and EB de Villiers
and without Coley.
So these were the five
and we got that five.
So yeah, there it is.
So it means
that we have made
all correct insights.
Now, what you can do is
that you can copy all
the sheets worksheets
that you have made and you
can include it in a dashboard.
So now we have created
all the visualisations
that we need in order to make
Insight but they are all
in different worksheets.
If you want to view
them all together
or maybe you wanted
for presentation purpose
you want to show it to
your senior your manager
who can view the entire thing
at once so for that
you need a dashboard.
In order to create
a dashboard you just go
and click this icon over here.
And this is
your blank dashboard.
So these are all the worksheets
that we have created.
So what we can do we can just
go ahead drag and drop all
these worksheets over here
so that we can have an entire
view of all the analysis
and all the visualization
that we made using Tableau.
So I'm just going to drag
and drop these sheets over here.
Now I'll just drop
the purple Gap.
You can also adjust the size.
There you go.
And you can go ahead and add all
the different sheets as well.
So this is how it is going to
look like so now I've adjusted
all the sites and I've made
the dashboard already.
So this is how it looks
like so I've got all
my worksheets here, whatever.
I want to view.
It's right here.
So this is how you can create
visualizations and put it
all in a single dashboard.
So that everyone can see
all the worksheet
and all the visualizations
that are created at once.
So this is what you
can do with Tableau.
This is how you
can create dashboards.
You can add more sheets to it
and you can adjust it
in different ways also.
Using functions in Tableau
is essential for being able
to represent your data
in the best possible way Tableau
luckily has a list of functions
that you can directly apply
to your uploaded data.
Now if you've used all
the functions such as SQL
or Excel these functions
should already seem
very familiar to you.
Hi all this is a pasta
from at Eureka
and in this module.
We are going to talk all
about Tableau functions now
in Tableau a user can We
use different types
of built-in functions,
which can be applied
to the following
kinds of parameters.
So before we begin,
let's look at the different
categories in which we
are going to be using
these Tableau functions.
So we have your number functions
followed by string functions.
Then we have date functions type
conversion aggregate functions.
And finally we have
logical functions.
So without Much Ado,
let's get straight
into the module.
So first of all,
we have number functions,
Number functions allow
you to perform computations on
your data values in the fields.
So these functions can only
be used with the fields
that contain numerical values
pretty obvious right
on your screens right now.
You can see all
the number functions
that I'm going to be covering
in this segment.
We will start with
the absolute function
or the ABS and close using the Z
and functions Each
of which I'm going
to demonstrate to you
using the Tableau desktop.
So let's get started.
But with the first function,
so the first function
in the number functions category
is EBS or the absolute function
on your left.
Most column.
You can see the name
of the function in the column in
between you can see its syntax
and on the right you
can see the description.
So what your abs does is
it Returns the absolute value
of the number or the parameter
that you put in the bracket?
So if I give like
a negative number in there
like a minus 5 then an absolute
function is going to turn.
To into a 5 and return
it back to you.
It's kind of like the mod
of a number in the example
that I have given you
can see we are trying to get
the absolute value
of budget variance.
So suppose you had a field
called budget variance.
The ABS function will return
the absolute value
for all the numbers
that are contained in the field.
So let me go
to my Tableau desktop
and I'm going to demonstrate
how this works.
So this is my Tableau desktop
and currently I'm using
the 2019 point one version.
This is the show me bar.
If you want to know more
about this interface
and how to use Tableau desktop.
We already have a few
videos a few tutorials
in our YouTube playlist.
Please feel free to go ahead
and check that out.
This tutorial is specifically
for functions in Tableau.
So we are going to die
straight into that.
So as you can see I
have already pulled in the data
from the sample data set
that is available in Tableau.
It's called sample Superstore.
Now.
This is only available
in the desktop version
and not in the public version.
So make sure if you're trying
to follow through
with this tutorial you are using
the actual Tableau desktop.
So what I'm going to do
is I'm going to navigate
to analysis and create
a calculated field this
what you see is the Ation,
editor that opens you're going
to name it absolute
as we are going to try
the absolute function right now
and I'll show you something
simple in the beginning.
It's as simple as this you're
going to go ahead and do abs
and suppose I put in minus 5,
which is the number
at the bottom.
It shows my
calculation is valid,
which is a great thing about the
calculation editor in Tableau.
It prevents you from making
further mistakes helps
you correct them immediately
because the bottom
it Going to prompt you
whether your calculation
is valid or not.
Then I'm going
to apply it and okay.
So basically if I
bring this here,
let me do the attribute.
Yes, so it will give
me the absolute number
instead of negative of 5,
it's going to give me five
because absolute is kind of
like your modulus.
It's going to give you
the absolute value of the number
that you have produced.
So let's try it with a field
that we have here.
So Let's go down.
So we have
this sales field here.
So let me remove this and you
can go make some changes.
So I'm going to edit this field
and instead of placing
just the number.
I'm going to put in sales.
It's automatically going
to appear to you.
That is how smart this tool is.
All right.
So when I bring this here,
it's going to automatically
show number here.
Now.
What I'm going to do is I'm
going to divide this by state.
All right, so we can see all
the absolute values over here.
Now, what I'm going to do is
I'm going to go to show
me and put it into a map.
So now we can easily go
to each state and see
the absolute sales
in all of these states.
We have South Dakota
North Dakota Montana
and all the other states
as you can see it shows us
the absolute value of the sales
in that particular State.
All right, and as you
can see on your right,
it will show you
the gradient of color
which basically means
that the lighter the color is
the lighter blue.
There is the less of the sales
are in that particular State
as opposed to the darker
the color as you can see
in the state of California.
Or it may be New York.
The signals are the maximum
no rocket science there.
Let's go back
to our presentation
and look at our next function.
So our next function
is the arc cosine
or ecos it basically
Returns the arc cosine
of the given number
and the result will
always be in radians.
So if you give an arc cosine
of minus 1 you supposed
to be getting something
in lines of one four one five
nine two six as simple as that.
So let's go back
to our Tableau desktop and run
this and see again.
We're going to go to analysis
and create a calculated field.
Your Google and type
equals minus one.
And apply it and like
the absolute measure
that we had discussed
about the previous time.
This also appears under measures
in the data pain just
like your other fields
and you can use it
in one or more visualizations.
So I'm going to bring this here.
And it shows three
point one four two,
which is just the rounded-off
version of 3.14159.
And that was all
about Arc cosine.
Next we have arc sine,
which as the name explains
Returns the arcsine of the given
number and Radiance.
I'm quickly going to show that.
here again And it gives you
the answer one point five.
Seven one.
Next we have the arc tangent,
which is given
in radians again.
It is a tan which is
the number function.
I will go through
this section pretty quickly
as these are not the functions
which are used very
regularly in Tableau,
but they were kind enough to
provide us with these functions.
So we are going to use them.
So I'm going to make
an edit in this again.
Just quickly going
to go through.
And you're supposed
to be getting 1.56 5.
Next we have ceiling now.
Basically what it does is
when you pass a decimal number
to ceiling it is going to round
it off to the nearest integer
of equal or greater value.
So if I give it
three point two,
six or something,
it's going to round it off
to straight up for now.
These are available
in a couple of data sources
like Microsoft Excel
and text file
and statistical files,
but they are also
not supported by a few.
Popular sources such
as Microsoft Access
and action vector
or Amazon Aurora so
on and so forth.
So here again,
I'm going to go to create
calculated field and I'm going
to name it ceiling.
Ceiling and then I'm going to
put on 9.12 6-5 just any number.
So as you can see the ceiling
what it did was it rounded
off that number to 810 right?
Let me try to So
that's all about ceiling next
we have caused now cause
or cosine of an angle.
It's pretty self-explanatory.
Most of you I'm sure
would have studied this
in school it basically Returns
the cosine of an angle
if you give
your angle in radians,
so what I'm going to do again,
so as you all might have studied
in school cos Pi by 4 is 1
by root 2 or point 7 0 7
so we can apply this.
Alright, so as you can see
it gives you point 7 0 7 1
which is 1 by root 2
which is again caused by by
for next you have caught
or cotangent of an angle same
as cause that you're going
to I repeat next
you have cotangent or cot.
And we are going
to implement it the same way
as we did cause I don't think
these trigonometric functions
need a lot of explanation.
We've all done these in school.
So Just get this over with.
So again, I'm going
to go with the pi by 4
because I'm lazy
and I'm not going to think
of any new angles.
So as we all know caught
by by 4 is 1 and
as you can see the value
immediately changed
to 1 the next function
we have is degrees.
Now what this does is it
converts a given number
in radians to degrees.
And as you can see Pi by 4
as we all know Pi is 180.
So your Pi by 4 naturally
is 45 degrees and that's
what it was supposed to do and
that's what it did moving on.
We have div or division.
So what it does is it's going
to return you the quotient
where your integer one is,
basically your dividend
and your integer to is
your divisor and it's going
to give you the integer part
of your shouldn't
so if I do 11 by 2,
it's not going to give
me a five point five,
but only if I've
as you can see your quotient
is 5 next you have
the exponent or exp.
Basically.
It returns erased to the power.
You're given number.
So basically if we start
with something simple,
like we'll just put
on a digits a arrays
to it straight up going
to give you erased to do
which is seven point three,
but you can also use
these to put in fields
and formulas such as
your growth rate into time.
So on and so forth next
you have floor now,
this is kind of the counterpart
of the ceiling function
where it rounds the number
to the Nearest integer
of the Lesser value
so like and ceiling
if I had to put in 3.1415.
It would give me a for a floor
on the other hand
would give me a 3 so
as you can see I have used
ceiling right here.
So, let's see.
What have we done for ceiling.
Okay, so I had put in
the number nine point one two,
six five four ceiling now.
Let's try putting in
the same number for floor.
So I'm just going
to make an edit
in this Going to put floors.
Okay, apply and OK and
as you can see immediately,
it went from ten to nine.
Our decimal has been rounded off
to the lower number.
Now again, like sealing
it is not supported
by Microsoft Access
your action vector and your
Amazon Aurora redshift so on
and so forth,
but it is supported by
your Microsoft Excel
and text file
and statistical files.
There are many other files
which support and also do
not support ceiling and floor,
but popular ones
like Google analytics
and Google big query support.
Both of these functions.
Next.
We have hex bin now,
basically what it does.
Is it Maps an x
and y coordinate
to the x coordinate
of the nearest hexagonal bin.
Now these bins have side length
1 so the inputs may need
to be scaled appropriately now
the heck Spandex and the hex
Why are binning
and plotting functions
for hexagonal bins now
these bins are efficient
and elegant options
for visualization of data in
the XY plane such as your Maps
or visualizations dealing
with geographical data
on your Scatter Plots
your hex plots now
because these bins are hexagonal
each bin is closely approximated
to a circle and hence,
it minimizes variation
in the distance from the data.
Point to the center
of the bin this makes
clustering both accurate
and more informaiton of next.
We have the natural logarithm of
a number basically you type Ln
and then your number
and it's going to return
the natural logarithm
if your number is less
than or equal to 0
it's going to return
a null value.
As we know
that the natural log of 1 is 0
so that is what we get.
That's the only one I
remember from school.
And that's the reason I put
that in as my input.
Now something very similar is
logarithm with a base
which we usually take as 10.
So if the base value is omitted
you decide to not put on a base.
It's by default
going to use base 10.
So we're going to put in
say a hundred and ideally we
supposed to get to and that is
it log hundred base 10 is 2
because trendiest to do
is hundred pretty simple.
Next.
We have Max which
as the name suggests.
It Returns the maximum
of the two arguments
which are passed.
Now the arguments must be
of the same type
and this function returns.
Null if either
of the argument is null so mad.
can also be applied
to a single field
if it is used as
an aggregate calculation,
which I'm going to cover
in the later segments
of this particular session,
so It can be as simple
as say two numbers.
So if I put it right here show
by attribute 7 is larger number
than 4 so here you have seven
or you could go ahead and put
into Fields over there.
Let's say profit and sales.
and when we Take the sum of it.
Now this I remember is the value
of sales obviously sales
is going to be more
than the profit.
The sales field is going
to have a higher number
than the profit.
So that's about it.
Now the other half
of this particular set
is the minimum function which is
like the maximum function,
but it Returns the minimum
of the two arguments
of the same type.
So if I just went ahead
and edited this particular,
Function instead of
Max let's name it men
and here again instead of Max.
Let's name it men.
Let's put in the Min function
going to apply it
and this I'm sure
is the prophet field
as you can see.
The number has changed
next we have pi
as the name might suggest
it is going to return
the value of pi.
Three point one four two,
which is the rounded off value
of pi next we have power.
So again, we are back
at our Tableau desktop,
and we're going to try
and Create a power field.
So here I'm going to just go
for power and then our base
which let's take 10
and then the power
so we'll take that as 3
and ideally our answer
should be a thousand.
So if I drag it to Rose
Bring it to its attribute.
We can see pauses equal.
Mm.
Next we have radians
which basically does
the opposite of degrees
it converts the number given
in degrees to radians.
So I'm going to go here again.
So earlier we had converted Pi
by 4 into 45 degrees.
So here I'm going to put
45 degrees and let's hope
we are getting Pi by 4.
So I'll take this rad
calculation put it here.
end we get 0.785
for which was also Pi by 4.
You can do the math use
the calculator take this moment
pause this you can go check
then we have round.
So basically what this does is
it rounds a particular number
that you put into a specified
number of digits, right?
The decimals argument specifies
how many decimal points
of precision to include
in the final result now
if that part you decide
to not fill anything
in the number is rounded
to the Nearest integer right now
before I move on to my Tableau
server some databases such as
SQL Server allows specification
of a negative length
where negative one rounds
to the tens place
negative two rounds
to the hundreds place and so on.
This is not true
for all the databases,
of course such as Excel
and access do not follow
through with it.
So with that let's go
to our Tableau desktop and see
how this functions So
what I'm doing here is
I'm going to round up
every sales value to an integer.
That's why I did not put
anything in the decimals place.
If I take
a subcategory, all right,
as you can see everything
has been rounded up
to an integer no matter
what the sales
in dollars is there might be
some decimals in there.
But all of it has been turned
into integers using
the round next we have sign
which Returns the sign
of a number now the possible
return values are negative of 1
if the number is negative
and zero if the number
of 0 and 1 One is
if the number is positive.
So I'm going to run
this particular example,
which I have given here.
I'm going to run that itself
on my Tableau desktop.
So I'm going to be taking
the average of the Prophet field
for fat and apply.
Now because the
profit is positive
in our given sample set.
Okay, this looks better.
Now.
What was I saying?
Yes now because
our profit is positive
in the given sample set
our answer turns up to be one
if it were negative.
It would have been
a negative of 1.
All right, after sign
we have sine which is
the trigonometric sign again.
So it basically returns
to you the sine of an angle
or we have to do is specify
the angle in radians.
So let's just So again,
I'm going to take
sine Pi by 4,
which is 1 by root 2.
So it comes up to be
0.707 same as your cosine,
then we have the square root
of a number which
as the name suggests Returns
the square root of a number.
So as we can see the square root
of 100 is 10 next we have
the opposite of square root,
which is the square of a number
pretty self-explanatory.
I'm just going to edit this.
Name of square.
I'm going to do
the simplest thing
and you might just think
this is a lazy woman,
which you're right, my friend,
so I'm going to square 10
so we can get a hundred
and there you have it.
It's a hundred no
big surprise there.
Would that we have tan.
Okay.
So here we have the sign
what I'm going to do.
I'm just going to edit
this not going to create
any extra sheet here.
Just going to put Dan
and change this into 10.
Tan Phi by 4 is 1
for those of you
who do not remember from school.
And here we have the answer as 1
and with that it
brings me to the final
of the number functions.
We have ZN which
Returns the expression
if it is not null.
Otherwise, it returns zero now,
we basically use this function
to use zero values
instead of null values.
So this is how it works.
I'm going to put in a field.
Which I know does
not have a zero value.
So I'm going to put in profit.
So here I'm going to put
in the average of profit.
It's in the some more now.
I'm just going to take
the average and I know
for a fact it's not a nonzero
value and its return
to me the average of profit
which is twenty eight point
six six with that.
We come to the end
of the segment that we head back
to the presentation to start
with the next module.
The second segment
is string functions,
basically string functions allow
you to manipulate string data.
Now you can do all sorts
of things with it like pull
out all the last names
from your customers.
To a new field one member
might be say shubham Sinha
and then you can pull
out all the customers
with the surnames Sinha and then
put them in a new field
using the string functions.
So again on your screens
are all the string functions
that I'm going to discuss
which is all
the string functions
that are available
in Tableau this by no way
is an advanced tutorial.
I'm just going to show you
how each syntax works.
So let's start
with the first one.
And we have a ski
as most of you know,
there is an ASCII code for
every character of the string
and what this function
does is it returns
that ASCII character?
Now as we all know
the stringy has the ASCII code
of 65 and that is
what this returns
next we have care
which is kind of like
the counterpart of ASCII here.
It's going to
return the character
that is encoded by
the ASCII code here.
You have to put in the number
and it will return
to you the character.
and as you can see,
it returns a next
we have contains
which basically returns true
if a given string contains
the specified substring.
And as you see it returns true
next we have ends
with which returns true
if the given string ends
with ascertain substring.
next you have find
which Returns the index position
of the certain substring
that you're trying to search
for in the string or zero
if the substring is in found
if the optional argument
which is start
as you can see at the end
of this argument bracket
is added then the function
ignores any instances
of the substring that appear
before that index position
start the first character
in the string is position 1
As you can see it's said
that the substring EK
that I was searching for starts
from index number 5 now,
let's see what happens
if we put in our last argument.
Let's go to 7.
As you can see
because till the 6th index
it was not asked to search.
It returns a 0
which means it could not find
the substring next
we have find in it
which basically Returns
the position of the anyth
occurrence of a substring
within a specified string.
So n is where the
argument has occurred.
Secure and find an it.
I've changed the word.
I've put in learning here
and I'm going to search for
when does n the alphabet N occur
for the second time
in the word learning.
All right,
so if I bring it to text
you see the answer 7
because L EA R ni NG.
It's an 8-letter word
where the second time and occurs
is at the 7th position now
left Returns the leftmost number
of characters in the string.
Ring so you put in the string
and then you put in the number
till which you want
your substring to be.
Let's see.
What do I like?
I'm going to put it
in Matt dough and then three.
So it's going to print
the left-most three characters
from the string.
Then we have a pretty
common one called length.
If either of you have used Excel
or word or access or any
of these common databases
and apps you know,
what alien does it returns
to you the length of the string?
So what I'm going to do?
Is there you go.
It Returns the length of Matador
which is 7 then you have
a function called Lower
basically you put on
a string here and it
will return you the string with
all characters in lowercase.
So I'm just going
to randomly capitalized.
Okay.
So next we have Ultram now this
basically Returns the string
without any spaces on the left.
So if I try to L
trim space matadors space it
is going to remove the space
at the left hand side
of your string
and here you can see the space
on the right is there
with the space
on the left is gone.
Then we have Max which Returns
the maximum of A and B,
which must be the both strings.
Now this function
is usually used to compare.
Numbers but it also
works with strings.
Basically what it does
is it finds the value
that is highest
in the sort sequence defined
by the database for the column.
It returns null
if I the argument is null.
So I'm going to put apples
and bananas just expression 1/2.
You can put anything.
And then it returns bananas
because obviously more number
of characters next we
have mid which Returns
the string starting
at the index position start.
So the first character
in the string is positioned one
if the optional argument
length is added then
the return string includes
only that number of characters.
So what I'm going to do
is I'm going to use
the word learning.
And then I'm going to start
with the second index number
which is supposed to be e
and then I'm going
to give a length
say five.
So five characters,
then I'm going
to apply it and okay
and as you can see
five characters starting
from position 2 next we have Min
which is the counterpart
of Max it basically reverses
what we got and Max it's going
to give you the string
with the minimum
number of characters.
So if you're going to do the
same thing apples and bananas.
You can also put in a field
in here and it'll give
you the least value
from that certain field.
So as you go apples having the
minimum number of characters,
then we have right
which Returns the right
most number of characters
that you have specified
in your syntax.
So if I put in learning
and for it should ideally give
me an Ing and there we have it.
Then we have the art room
which is kind of like the L trim
but from the right,
so if I put in a string
with spaces on the right,
it's basically going
to clear out the spaces.
You're going to type
the same word spaces
on the left Android both
and when we get it you can see
there is a space on the left
but not on the right,
which is the opposite of L trim
where we deleted
the space in the left
and we kept the space
and the right next
you have space
which basically returns a string
that is composed
of the specified number
of repeated spaces.
So space one is going to give
you one space to was going
to give you two spaces so on.
So forth then we have split now.
This is an interesting one.
It Returns the substring
from a string using
a delimiter character
to divide the string
into a sequence of tokens.
Now, the string is interpreted
as an alternating sequence
of delimiters and tokens.
So, let's see how this works.
and I am going to C
and T Let's put these quotes
on both sides.
And then I'm going to put in
what my delimiter is
which is the -
now I could have also used.
/ the only thing you
have to do is specify it
in the delimiter part
of the syntax
and then I'm going to put
in the token number two,
which otherwise should have been
my delimiter this one the -
but because we specify
the delimiter supposed
to be returning a b and
as you can see it returned
be now the split
and the custom split commands
which are Kind of the same
commands are available for
if you data types
and they're not available
for a few data types.
So oo data extracts
Microsoft Excel text file
PDF file Salesforce all
of these support the split
and some data sources impose
limits on splitting string
and there are these ones
with support- tokens
where there is a limit
on the number of splits
that is allowed per data source
next we have trim now.
Basically trim is
the combination of L trim
and or trim it is going
to remove spaces
from both left and right.
I'm going to again use
as you can see space
and matador and another space.
And as we can see,
it has no spaces
in the beginning
or the end and here we are
at the final string function,
which is upper
which Returns the string with
all characters in uppercase.
So I'm going to go here.
And go with upper.
And randomly capitalized
like I had done earlier.
Let's see.
And it returns to us
Matador in all caps.
So with that we come
to the end of string functions.
The next segment is
on date functions.
Now as the name suggests
the date functions allow
you to manipulate dates
in your data source such as
your month date date or time.
So let's go
to our first function.
We have the date add
now the data are returns
a specified date with
the specified number interval.
Added to a specified
date part of that date.
For example,
if I put in month as
the date part 3 as the interval
and it date as
the date this expression
would add three months
to the date 15th of April 2004.
So let's try this.
You know this expression adds
three months to the date.
So we had zero for as
an April for this date.
But if I change this
to month it has July,
which is May June July edition
of three different months.
Next we have date diff
which Returns the difference
between the two dates
that you have input
into this function.
The start of the week
parameter is the one
which you use to specify
which day is to be considered
the first day of the week.
It is optional, of course,
but in a lot of countries
you consider Monday as
the first day of the week,
whereas in the other part
of the world you consider Sunday
as the first day of the week
if this part is omitted
then the start of the week.
Is determined by
the data source, so,
let's see how that works.
So as the date part,
I am putting in we then I'm
going to put in 2015 see 2015,
October and let's see 23rd.
and then I am going to put 2015
and the same thing
on the 11th month November.
I'm going to put in
the start date as Sunday.
Alright, I think
with that we're done.
And we're going to change this.
And it shows
that the difference is 5 weeks.
Next we have date name
which Returns the date part
of the date as a string
and the start
of the week parameter again,
like the previous function
you can use to specify
which day is considered
the first day of the week.
It could be Sunday
or Monday or Tuesday
depending on what you like.
So if I put in the date part
as ear and I put in the year
and the starting date
of the week,
which in this case is
not really necessary.
It returns a string 2004,
which is the name of the earth
that we had put in next.
We have date part which kind of
does the same thing
as the date name,
but it Returns the date part
of the date as an integer
instead of a string.
So if I made that edit here.
It was returned
to me the same thing
but it is not a string
but an integer
next we have date trunk.
Now as a lot of you might
have guessed this basically
truncates the specified date
to the accuracy specified
by the date part.
So basically this function
returns a new date, for example,
when you truncate a date
that is in the middle
of the month
at the month level this function
will return the first day
of the month again.
This has the star
The week parameter
which you can use to specify
which day is considered to be
the first day of the week.
So on and so forth.
So I've put on this date
and I've specified quarter.
So it's supposed to truncate
this part of the date
completely and bring me
to the beginning of the month.
So when I bring this here So
as you can see,
it has brought me
to the beginning of the month
July and has truncated that part
of the month from half
the month 15th of July
to the first part
then we have day
which Returns the day
of the given date as an integer.
As you can see it's 15
next we have is date.
Now this returns true
if a given string
is a valid date.
so if I put in February 30th
2015 it returns false
because there is no 30th
in February on the other hand.
If I go ahead edited and put in
like a 28 to Feb 18 turn stru
because 28th of February
this tin every single year.
Next we have make date
which returns it
is value constructed
from the specified
your month and date.
So if I make date with 2015
3 and 15 I'm supposed to be
getting 15th of March 2015.
This is a slider motive
of the previous function.
This is basically an extension
of make date time
and along with the date.
It adds the time as well.
Next we have make time
which is exactly like make date
except for its for time.
So it returns an arm minute
and second off the time
that you put in now going
to spend much time here
as they are all
pretty similar in nature.
Next.
We have Max that we have.
Two times before so
I'm not going to demo
this is well again,
you will put into dates
and it is going to return
the maximum of the two dates.
Next we have men again,
you're going to put in today.
It's and it's going to return
to you the minimum
of those today's
next we have month
which Returns the month
of the given date as an integer.
So if I put in 2005
0 9:23 I'm close this.
It gives me back nine
which was the month.
I had put in.
So this is an interesting one.
This is called now this Returns
the current date and time.
Now what it returns varies
depending on the nature
of the connection.
So for a live unpublished
connection now Returns
the data source
server time and for
alive published connection
now Returns the data
source server time,
so I'm assuming
that it is going to give
you my system time.
So just going to go with now.
I gives you 2019
if I bring it down to the date,
it's going to show
7th of May 2019,
which is the day in which
I'm recording this video.
Another one like now
is today and basically
what it does is it returns
to you the current date?
So instead of now
we're going to put in today
and it gives you
today nice isn't it?
And finally the last
date function we are going
to do is you're so
basically it Returns the year
of the given date as an integer.
And if I bring this here,
it gives me 2014
which is the year.
I had put in and it's given
it back to me as an integer.
Next.
We have type
conversion functions.
Now, why do you use
these functions now
that conversion functions allow
you to convert fields
from one data type to another.
So basically you
can convert numbers
to Strings such as age values
so that your tablet
will not try to aggregate them.
So let's move on to our first
type Version function
the first one is date now.
Basically this returns
a date given a number string
or date expression.
So if I go in And put
an order date.
Basically has given me a list
of all the order dates
that are there in the data set.
So I can obviously
filter this by ear.
And bring it down
to these many dates.
Next we have daytime
which is kind of like date
but it returns a date.
I'm given a number
or string now the some number
show you the demo
because it is very similar
to the date function.
Now we have date parse.
Now what this does is it
converts a string to a daytime
in the specified format.
Let us that appear in the data
and do not need to be par
should be surrounded by
single quotes and for formats
that do not have delimiters
in between them suppose.
Fins and slashes
and dots verify that they
are passed as expected.
So the format must be a constant
string not a field value
and this function returns null
if the data does
not match the format.
So, let's see how this works.
So this is my format.
And this is my date 15.
September and 2005
And this is exactly
how it's going to show it to me.
This is the same in the table
next we have float.
So basically if I enter a few 3
it is supposed to give me 3 .00
next we have int or integer
which again opposite
of float going to cut
down the decimal part
of the number and give
me only the integer part.
And finally we have
a string expression.
Basically you're going to pass
in an argument through it
and it's going to convert
it into a string
and give it back to you.
So that's all
about type conversion.
Let's go back here
for the string.
I'm going to give you a demo.
Rest all is kind of the same
how we had done before and
how you do it with other tools.
So basically what I'm going
to do is I'm going
to edit this and SDR
and I'm going to put
in the postal code.
Now as we all know
postal code is in numbers.
So as you can see,
this is our postal code and it
comes He's under Dimensions.
Why because it is currently.
Number as we can see,
it's a whole number
and what we gonna do.
We're going to edit this Str.
String and put in
postal code and okay.
Now as you can see
the type conversion,
which is basically the same
thing as the postal code has
a string data type.
The postal codes
will remain the same,
but their data type
is going to change that is
what the string function does.
And with that we come to the end
of type conversion functions.
Let's move on
to aggregate functions.
There are a few more
than the type functions here.
So basically what aggregate
functions let you do is
That they allow you to summarize
or change the granularity
of your data.
For example, if you
want to know exactly
how many orders your store had
for a particular year you
can use the count D function
to summarize the exact number
of orders your company had
and then break
the visualization down by ear.
So there are a bunch of
aggregation function out here.
Most of which we
have already used as
number functions before and
if we haven't used them as
number functions before for
while we have dragged it
to our marks panel.
Most of you have seen
how they work.
So let's start
with the first one.
The first one is attribute.
You might have seen
through the course of this video
how I have changed the default
aggregate from some
to attribute many times over
in this video itself.
It basically Returns
the value of the expression
like it has a single value
for all the rows.
So it is like one standard value
the null values, hence.
Ignored next as
the name suggests.
This is the average expression.
It Returns the average of all
the values in the expression
since I haven't used it
before I am going to show
how this works.
So suppose I bring
in my sales here
as you can see
the default measure is
some I can break it down
to the average and it shows
average sales nothing new.
No rocket science next we
Have correlation coefficient
which basically Returns
the Pearson's correlation
coefficient for two expressions.
Now the Pearson correlation
measures the linear relationship
between two variables
and results range
from minus 1 to plus 1
where 1 denotes an exact
positive linear relationship as
when a positive change
in one variable implies
a positive change
of the corresponding magnitude
in the other now 0 denotes
no linear relationship
between the And negative 1
is the exact negative
of a relationship.
Now.
What I'm going to do
is I'm going to use
a table scoped level
of the detail expression.
So I'm going to go ahead
and And this is
what it gives it's
between negative 1 to positive 1
and this is how far
the relationship is linear.
So you can say that
the relationship between profit
and sales is fairly linear
because it's on the positive
side and with a level
of detail expression
the correlation you
can run all over the rose
and the view would
show the correlation
of each individual point
in a scatter plot
if you want to do that.
Next we have count
which Returns the number
of items in a group null values
are obviously not counted
and then we have count D
which Returns the number
of distinct items in a group
which basically means
that if a certain item is there
twice in a certain group count
D is going to count
it as one item.
Then we have
the covariance expression.
This basically returns
a sample covariance
of two expressions.
It's basically quantifies
how two variables change
together a positive
covariance indicates
that the variable tend to move
in the same direction.
When a larger value
of one of the variables tend
to correspond to a larger value
of the other variable
on an average.
Now the sample covariance
is the appropriate Choice
when data is random
and it is being used
to estimate the covariance
for a larger population.
Like we found out
the correlation between sales
and profit we can also find out
the covariation between sales
and What next we have
Co variance of the population
it basically does
the same thing as covariance.
But this time
of the entire population next.
We have Max which
we have tried Thrice
before it Returns
the maximum expression
across all records.
We have median which Returns
the median expression
across all records.
And then we have the main which
Returns the minimum expression
across the records next.
We have percentile
what it does is it returns?
The percentile value from the
given expression corresponding
to the specified number now
the number must be between 0
and 1 obviously
and this function is available
in Microsoft Excel
and text file
connections Google analytics
or Salesforce Cloudera
Hive Oracle 10 and deleted
data resources of Oracle exist
solution 4.2 and the later
versions and sybase.
Next we have standard deviation
which Returns the statistical
standard deviation
of all values.
In the given expression
based on a sample
of population now the same thing
for the entire population
and not the sample is
the standard deviation
of population it basically
Returns the statistical
standard deviation
of all values
in the given expression based
on a biased population.
Next you have some
which is also
the default aggregate
which is allotted to all
the measures and dimensions
that you use it
Returns the sum of all,
All values in an expression
and of course it can be used
only with numeric fields
and null values will be ignored
then you have variance
which Returns the statistical
variance of all values
in the given expression
based on the sample
of your population.
And finally we have
the population variance
which gives the statistical
variance of all the values based
on an entire population
and not just a sample.
We're finally at our last
segment logical functions.
Now why should you be using
logical functions now?
This is basically used
to calculate Boolean logic.
If you don't know what that is,
it basically means you give
a certain condition
and it tells you
whether it's true or false
there is no other output
than true or false.
Now most of these things.
We've already seen
through the many functions
that we have covered today
and the rest are very
common logical functions
if you are from
any coding background,
so what I'm going to do
For this particular segment is
that I'm going to go through all
of these functions first
and then go to my Tableau
desktop to show you a tiny demo.
So first of all
and function now basically this
performs a logical conjunction
of the two functions.
So basically it's the logical
and if you look
at the given example,
it says if your Market
is equals to Africa
and the sum equals
sales both are greater
than emerging threshold,
then it should give the output
while performing Notice
how both of these conditions
have to be true
for Tableau to give you
an output of well-performing
that is what a logical and does
for logical and to work.
Both of the conditions
need to be true.
Next you have case
if anybody of you are
from the coding background,
you can relate it
with the switch case.
It performs a number
of logical tests
and returns appropriate values
the case functions evaluates
Expressions Compares it
to a sequence of values.
And returns a result
when a value
that matches the expression
is encountered case Returns
the corresponding return value.
If no match is found then
the default:return expression
is used and if there
is no default return
and no values match,
the null is returned case is
often easier to use then if else
and statements like that.
Typically if you use
an IF function to perform
a sequence of arbitrary test,
you can use a case function
to search for a match.
When expression but
a case function can always
be re-written as an IF function.
Although the case function
will generally be more concise.
So many times you can use
a group to get the same results
as a complicated case function.
We learn more about
if in the later parts
of this logical functions
segment next we have else now.
This is the counterpart
of if so,
if something then a condition
has to be either Old
or not fulfilled it
will give you an answer else.
It will give you another answer.
It's as simple as that,
it tests a series of Expressions
returning the den value
for the first true expression.
Then you have else
if if you have a number
of if statements
before your default else,
then you use else
if it tests a series
of Expressions returning
the then value for the first
row expression again,
then you have end which
as the name suggests.
It marks the end
of an expression.
Then you have if as we
have already discussed
if basically tests in expression
and then returns
a corresponding value,
then we have
if null which Returns
the expression one.
If not null otherwise returns
expression to it is basically
like a regular IF function
except for it just checks
for one condition.
If the expression you've put
in is null or not.
Next is I if which checks
weather condition is met
and turns one value
if true another value
if false it's basically
like a test case
and an optional third value
or null if unknown next
we have is date
which returns true
if a given string
is a valid date.
It's no big deal.
Then we have is null
which returns true
if the expression
does not contain valid data,
which is if your expression
is null then we have Max
which we have discussed
many times before it returned.
Maximum of all the records
or maximum of two expressions
for each record.
Then we have minimum
which does just the opposite.
It Returns the minimum
of all records
or the minimum of two records.
Then you have the logical not
which performs a logical
negation on an Expression.
So basically if the field you
have put in does not fulfill
the condition then it gives
you the corresponding result.
Then you have the logical
or which basically Means that
if you have put into conditions,
either or of them
have to be true,
then it will give you a result.
Then we have then
which we have discussed before
if a certain field fulfills
a condition the corresponding
then part is then fulfilled now
here we have a venn function
which finds the first value
that matches the expression
and Returns the
corresponding return.
It's kind of like
if and then case
except for there is no
if Then you have ZN
which returns your expression
if it is not 0 or null we
have discussed this before now,
let's go to our Tableau desktop
and create a logical
calculation to see
how you can use this.
So now again come back
to our Tableau desktop one
last time for this session.
So again, I am
from the data pane drag state
to the Rose shelf as you can.
In see a table,
like this should ideally
appear then I am going to take
category place it at the Roses.
Well, this is nice.
I'm going to analysis
as I've done many times before
in this session create
calculated field here.
I'm going to name this kpi,
which is key performance index
basically shows you
how your company
is doing internally
and externally internally
azing different departments
and externally as in
in the market going
to do is see some profit
we can see this here.
Now this calculation
quickly checks.
If a member is greater
than 0 if so,
it will return true.
If not, it will return false.
It's kind of like the if
and then statement so
when finished you can go
ahead and click OK
as you can see this new
calculated field appears
under the measures
and the data pin just
like your other fields you can
use it on this visualization.
So I'm going to drag this
to the color on the mocs card.
animal turn this whole thing
into so I'm going to take
the sales and put it
in the columns.
You can now see which categories
are losing money in the states.
So all these orange ones
are the ones in profit
and the blue ones
are the ones at loss.
And that's the simplest use
of The Logical functions.
You can use this for way more
because Tableau is capable
of not just pretty graphs
but a good drill
down of data with that.
I am going to close the session
Tableau was made with an intent
to analyze data in a much
easier and efficient way
than we were doing
all these years
with these traditional methods.
But if you have to stop thinking
about how to use the tool
to solve a problem the state
of flow is broken
one common cause
of this is the need
to work with data
that has been aggregated
to different levels of detail.
Hi.
I'm a pastor from Eddie Rekha.
And today we're going
to talk about levels
of detail in Tableau
before we begin.
Let's look at the
agenda for today.
First up.
We are going to talk a little
bit about what aloni actually is
what it does then we'll talk
about the various
calculation in LOD
which are The
include the exclude
and the fixed calculation then
we can talk a little bit
about aggregation
and LOD Expressions
followed by nesting
and inheritance in LOD.
Then we're going to talk
about the data sources
supported by level
of detail and Tableau.
Finally.
We have a short demo
on how to create some simple
LOD expressions in Tableau.
Then we're going to talk
about table calculations in LOD
and finally discuss
a few limitations of Valerie.
So without Much Ado,
let's get straight
into the module now
the Few questions,
which always arise
while dealing with data
that has been aggregated
to intricate levels of details
and these questions
are often simple to ask
but really hard to answer
the sound something
like can I plot the number
of days per quarter
where my company had
more than a hundred orders?
How can I find the biggest deal
each salesperson has ever
closed then show
the averages by manager.
How can I tag every customer
by the year he or she
first became a customer
and then use that tag
to group The Sales,
in order to address these types
of questions Tableau 9.0 onwards
introduced a new syntax
called the level of detail.
Now this new
syntax both simplifies
and extends tableaus
calculation Language by making
it possible to address level
of detailed questions directly.
So in simple terms level of
detail Expressions represent an
elegant and Powerful
way to answer questions
involving multiple levels
of granularity in a single.
Sure now granularity
and aggregations in LOD
or inversely proportional
to each other
how it works is the
more the level of granularity.
The less is the level
of aggregation and vice a versa.
So here are the shelves
which affect your LOD Aggregates
you have your columns rows
pretty much everything
except for your pages
and filter shelf
and these are the shelves
which do not affect
your LOD Aggregates.
These are your pages filters.
And tooltip now all
these LOD expressions
are segregated into three types.
We have included
which basically calculates
at a lower level of detail.
We have fixed LOD expressions
with specify the exact level
of detail and we have
the exclude level
of detail Expressions,
which calculate at
a higher level of detail.
So first, let's talk
a little bit about
how you work a problem
in level of detail.
Now this map shows
the restaurant inspection.
Doll from Yelp
in the greater Edinburgh area.
So the data in the view
is aggregated based on the LOD
which in this case consists
of city and state
and is more aggregated
than the underlying data source.
So the selected point
in the image shows
the average user fans
for all the restaurants
in Newbridge Edinburgh adding
more granular Dimensions
to the view will result
in a less aggregated LOD.
For instance.
We could add business ID
to the visualization by popping
it on the detail shelf to see
the average user fans
for each individual business
by doing so will also
change the visualization
every single business will
appear as a circle on but what
if we don't want
the visualization to change
what if we want to determine
the total user fans
for each business ID
and average those values
for each City and finally
show only one Circle per City.
What we want to see is
the average number of fans
per restaurant in each.
Now this will require adding
an additional Dimension
to the view without dragging
that Dimension into
the visualization
a level of detail expression
will allow us to do this.
Now this expression
tells Tableau to
perform the aggregation
for each business ID regardless
of other dimensions
used in the LOD,
you can use this expression to
calculate the total user fans /
business ID after dragging
this new field into the view.
We can then average those values
per setting now the fans /
business field have been added
to the color shelf
as you can see on your left
now new bridge has
the highest average fans
per business with a hundred
and eighty five pans a value
that was computed using
the fixed LOD expression
by using the fixed operator
in our LOD expression.
We gain insight into which
cities have on average more fans
per business ID
meaning those cities
with a darker shade of blue
have more popular restaurants,
or maybe even the city
could More residents
and hence more total
fans / restaurant.
Now what you see up top is
how the level of detail
expression is structured.
You have your scoping keyword
and then you have the dimension
declaration and finally
the aggregate expression.
So you're scoping keyword as
in your include exclude
or fixed your dimension
declaration in this case
your business ID
and your aggregate expression is
what you want to do
with that declaration
like Here you are going
to use some expression here
is the structure given more
clearly for the level
of detail expression.
Now as a result of that fans
per business field
have been added
to the color shelf and it shows
that Newbridge has the highest
as computed using an LED
expression average fans were
business with a hundred
and eighty-five fans.
Now, let's talk a little bit
about the include calculation.
So the include LOD expression
will add Dimension
to the LOD then Keyboard
creates an expression
that is less aggregated,
which also means it is
more granular than the LOD
the specified dimension
of first added to the LOD
before the calculations
are performed.
Now notice that the include
expression is used in The View
as an aggregated measure.
In fact, all include expressions
are either used as measures
or aggregated measures
when placed on The View,
let's talk a little bit
about the exclude calculation
now exclude calculation.
Basically means calculating at
a higher level of detail using
an exclude keyword will exclude
the desired Dimensions
from the calculation Tableau
first removes the excluded
Dimension from the LOD
and then performs
a calculation as
of the dimension was
not present at all.
The result is then
displayed visually
the graphical representation of
how Tableau performs
an exclude LOD expression
is depicted in the diagram
that you can see next.
Let's talk about
the fixed calculation now
LOD Expressions also.
Oh The door to creating
an aggregation level completely
independent of the LOD something
that was previously
only possible by the custom
structured query language
presenting to you.
We have the fixed calculation
which basically specifies
the exact level of detail
this LOD expression
will fix the level
of detail to each Dimension
and does the aggregation
specified in the
calculated field regardless
of any dimension in the view
if you look at The sequence
of filters and Tableau
fixed calculations are applied
before Dimension filters.
So unless you promote the fields
on your filter shelf
to improve view performance
with context filters.
They will be ignored include
and exclude level of detail
expressions are considered
after Dimension filters.
So if you want filters to apply
to your fixed level
of detail expression,
but don't want to use context
filters consider rewriting them
as include or exclude now,
let's go ahead
and look at aggregations.
I'm level of detail now
the level of detail of the view
determines the number
of marks in your view.
So when you add a level
of detail expression
to the view tab low must
reconcile two levels
of detail the one in the view
and the one in your expression
the behavior of a level
of detail expression
in the view varies,
depending on whether
the Expressions level
of detail is coarser finer or on
the same level as the level
of detail in the view.
So what do we mean by Corsa
or fine and this case
so if I said the level
of detail Action is coarser
than the view level of detail.
I mean that it references
a subset of the dimensions
of the view.
For example for view
that contain the dimensions
category and segment you
could create a level of detail
that uses only one
of these Dimensions.
So when I use
this expression here,
the expression has
a coarser level of detail
than the view it bases
its values on one dimension
that is segment.
Whereas the view is basing
its view on two dimensions.
I'm category.
The result is
that using the level
of detail expression
in the view causes
certain values to be replicated.
That is they will
appear multiple times.
Now if I say the level
of detail expression is finer
than the view level of detail.
It references a super set
of Dimensions In The View
when you use such an expression
in the view Tableau
will aggregate results up
to the view level.
For example, the following level
of detail expression references
two Dimensions when this occurs
Expression is used in a view
that has only segment
as ass level of detail.
The values must be
aggregated here is
what you would see
if you drag the expression
to a shelf and aggregation
in this case
average is automatically
assigned by Tableau,
but you can always
change the aggregation.
If you need it now adding
an LOD expression to the view
whether level of detail
expression is aggregated
or replicated in the view
is determined by the expression
type which is fixed.
Execute and
whether the Expressions
granularity is coarser
or finer than the views now
for include the LOD will have
either the same level
of detail as the view
or a finer level
of detail than the view.
Therefore values will never be
replicated for fixed level
of detail Expressions
can have a finer level
of detail than the view
a courser level of detail
or the same level the need
to aggregate the results
of a fixed level of detail
depends on what dimensions
are in The View.
And finally for exclude level
of detail Expressions
replicated values will always
appear in the view
as we had discussed before
when calculations include
the exclude level
of detail Tableau defaults to
the attr aggregation to indicate
that the expression
is not actually being aggregated
and that changing
the aggregation will have
no effect on The View now,
let's discuss a little nesting
in the level of detail now
Tableau does not limit you
to write single simple.
You can Nest as many Expressions
as you want according
to your requirements.
So when you approach
this kind of a problem,
you have to understand
a few rules that come
with this inheritance property.
So there are two
types of inheritance
in table calculation.
One is the impact
of fixed expression,
which we had earlier
mentioned has impacts
on where it is evaluated
and which filters affected
and then there is
the dimensionality itself.
So in this case,
if you look at the first one
I am doing a fix State
and nested calculation
I'm saying include customer.
So include as you
all know in head is
from its surroundings,
if you build the dragging
and dropping by itself,
it will inherit
from the parent calculation.
So it will include
the state it will do
the same things as writing
State customer instead of it
and it won't be
impacted by filters
because the parent is
a fixed calculation with that.
Let's move on
to the data sources
that are supported
by level of detail.
So here I've made a list
on Data sources and
whether they are supported
or not supported
by level of detail.
Now, let's move on to a Hands-On
of how to create level of
detail expressions in Tableau.
Now, the question is
how to create these LOD
expressions for that.
We will have to move on
to our Tableau desktop.
So let's head there now
for your LOD expression
first up prerequisite is
that you need to have
a visualization already set up.
I've created a very simple
bar chart over here with three.
Agents from the dimensions
data pane in the columns
and sales in the Rose shelf
now Step 2 instead of sum
of all sales per region,
which is given by Tableau by
default this one right here.
Perhaps you would also want
to see the average sales
per customer for each region,
but this you can use
an LED expression.
So I'm going to go to analysis
and create a calculated field.
In this calculation editor
that opens I'm going to name
this sales per customer
and then I'm going to put
in this expression.
So include customer name.
And then I'm going to apply it
to my visualization and click.
Ok.
So the newly created
LOD expression is added
to the data pane
as you can see.
It's right here
sales per customer
under the measures pain.
Now we are going to use
this LOD expression
in the visualization.
So from the data
pane under measures,
I'm going to drag this sales
per customer to the Rose shelf
and place it to the left of some
of sales see right here
on the same, Rochelle.
I'm going to right click
on sales per customer
and select the measure some
and then take the average
you can now see both the sum
of all sales
and the average sales
per customer for each region.
For example, you can see
in the central region
that the sales totaled
approximately $500,000
with an average sale
for each customer being
approximately 800 US Dollars.
Now this was all
about the include expression.
We're going to do the same
for exclude and fixed.
now for exclude expression,
we're basically going to try
and exclude region
from a calculation of some
of sales and to illustrate
how this expression
might be useful first
consider the view that you see
on your screens right now,
which breaks out the sum
of sales by region and by month,
so I'm going to be creating this
expression the same way I did
before You see a new measure
being created here
called exclude region.
Now, we are going to drop
exclude region this measure
that I just showed
you on color Shades,
which is right here.
We basically showing
the total sales by month,
but without the regional
component, I'm going to change
the colors a little bit.
So it's more prominent.
So let's go with this orange
and blue Divergent color.
All right.
So this is how the exclude
expression is created.
Now, let's move
on to the fixed expression,
which is another sheet.
Now as I had mentioned
before a fixed level
of detail expression
compute some value
using specified Dimensions
without reference to
the dimensions of the view.
So the fixed LOD expression.
I'm going to use now computes
the sum of sales per region.
So we're going to do
the same thing,
which is go to analysis
and create a calculated field.
As you can see this measure
has been created here.
So what I'm going to do
right now is I'm going
to take this measure
and please sit on the text to
show the total sales per region.
So the view level
of detail is region
plus state but
because fixed level
of detail Expressions
do not consider
the view level of detail,
the calculation only
uses the dimension
that is referenced
which in this case is region
because of this you can see
that the values Individual
states in each region
are identical with that.
Let's switch back
to our presentation.
This is a very common question
of how the level
of detail compares
to the table calculation.
And now that we have level
of detail do we actually need
the table calculations?
The answer is yes,
you will still be needing
table calculations,
but the LOD is here
to take care of a lot more
of the things now.
First of all table calculations
are generated by query results.
Whereas the LOD expressions.
Are generated as a part of query
to underlying data source table
calculations can produce
results either equal
to or less granular
than said LOD Dimensions
that control the operations
of a table are separate
from calculation syntax
in table calculations.
Whereas in LOD
Expressions Dimensions
that control the operations of
analog expression are embedded
in the expression itself.
Another big difference is
table calculation can be used
as aggregated measures
and The Expressions
can be doubled for various
other constructs the filters
on the table calculation act
as a hide whereas filters
on the LOD act as an exclude.
And finally, let's
discuss the limitations
of level of detail.
Now the a few limitations
and constraints that apply for
the level of detail Expressions.
Now level of detail Expressions
that reference floating-point
measures can behave unreliably
when used in a view
that requires comparison
of the values in the expression
the LOD expressions
are not shown
on the data source page
while referencing a parameter
in a dimensionality declaration.
We cannot use parameter values.
We have to always use
the parameter name.
So if you do not know
the parameter name,
then you going to face a problem
with dimensionality declaration
and finally would data
blending the linking field
from the primary data source
must be in view
before you can use a level
of detail expression
from the I can read
data source, in addition.
Some data sources have
complexity limits and Tableau
will not disable calculations
for these databases.
But query errors
are a possibility
if calculations become
too complex with that being said
LOD expressions are a powerful
new capability of Tableau,
which allow us to
easily solve problems
that previously required
very complicated formula.
They allow us to intuitively
define the scope of calculations
and stay in the state
of flow as we Our data
they are not a new form
of table calculations,
even if they can
replace many of them,
but they do open doors
to new possibilities contrary
to popular belief elodie's
and table calculations
operate very differently
from each other.
And finally, I'd like to say
that LOD Expressions represent
a vital step towards the goal
of complete flow
where all questions are simple
and elegant to answer.
It has been well established
that Tableau is not just meant
for pretty visualizations.
It is also helpful in intricate
calculations aggregate functions
and many more
drill down procedures.
Hi all this is a pasta
from Eddie Rekha.
And today we are going to talk
about one such feature
in Tableau called a parameter.
So we're going to start
out by discussing
what our parameters in Tableau
then we are going to move out
to our demo machine,
which is our tableau.
Desktop we are going
to start there by connecting
to our data sources.
Then we are going to create
a parameter in Tableau.
Then we are going to use
the parameter in a calculation
learn a little bit
about parameter control.
And finally we're going
to use our parameter
in our visualization and see
how it affects our data now
before I go much further.
Let me request you
all to go ahead and hit
that subscribe button.
So you never miss a new weekly.
Deal from your
favorite tech Channel
at Eureka YouTube channel.
So without Much Ado,
let's get started.
So what exactly is a parameter
in Tableau think
about it like this.
What if you need a component
for your visualization
that is not exactly
in your data set parameters
and Tableau will allow
you to provide that value
which you're going
to pass to Tableau.
Now this particular feature will
allow you to use aggregated.
Use that aren't readily
available in your data set.
It will help you
incorporate these values
into your dashboards
and reports directly.
Now after creation
and users can control the import
to see the results of the effect
of the parameter easy, isn't it?
So what exactly is
a parameter now any value
that is passed to your program
in order to customize it
for a specific purpose
is called a parameter now,
it could be anything.
Say a string of text
a range of values
or any amount in rupees
or dollars just to name
a few parameters will help
you experiment with some what
if scenarios suppose
you are unsure
which feels to include
in your view and which layout
to not what layout
would work best
with your viewers giving them
the choice you can incorporate
parameters into your views
your charts your graphs
and your calculations
to let your viewers.
Is choose how they want
to look at your data.
Now when you use parameters
it is of utmost importance
that you need to tie
them to the view
in some way one way to do.
This is why our calculations
you can use calculated fields,
which are Incorporated
in your visualizations
in Tableau second.
You can display the parameter
control in the view
for your users to select
from the parameter now finally,
you can reference parameters
in parameter actions.
Which basically means
you can use them
in your graphs and see
the effect it has on your data.
So now that you know a little
bit about what parameters
are just theoretically knowing
about this concept wouldn't
obviously do you much good so
the next few segments I
shall carefully guide you
through the process of creating
and using these parameters in
Tableau from this point onwards.
We are mostly going to be
on our demo machine,
which is Tableau desktop.
So let's get Started
so when you open your Tableau,
it kind of looks like
what you're seeing
on my screen right.
Now.
What I'm going to do
is I'm going to connect
to the sample Superstore
that is already
provided by Tableau.
Now why I'm doing this is
so that you guys
do not have a difficulty
in finding the data set
that are using you
can directly go connect here
and follow along.
Now.
This is what my data
set looks like.
I have the sample Superstar,
which is basically a collection
of many stores spread.
Across the United States,
it gives you the country city
and state the customer name
your shipping details your order
details your categories
and subcategories
of your products.
Basically all your sale
information sales discounts
profits profit ratio.
So on and so forth.
This is what
your data looks like.
Now, let's move onto our sheet.
So by no means am I
my going to give you
like a full beginning
with Tableau tutorial.
We already have a few
of those kind and we have
one coming up pretty soon
and updated one
so you can go ahead and look
at that in our playlist.
So basically what I'm going
to start out doing is
I'm going to create
a basic graph.
So let's see.
I want to create a sales
according to the order
date sort of a graph.
It's going to be a line graph.
I'm going to put the order date
and my columns and there
is a measure called sales
that I'm going to put
on my rose shelf.
It gives me a graph like this,
but this isn't very informative
as we just have four years now.
I want a more elaborate graph.
So I'm going to go ahead
and click on the spill
with says ear.
I'm going to go to the options.
Select more and then
go to custom.
Okay.
Now this is the custom
date dialog box
as you can see it only has
the auctioneer selected.
I'm going to go down
and I'm going to go
select month / here.
So it's going to show
me all the months
through these four years.
All right.
So now as you can see,
we have all the months
from 2015 through 2018.
We have a much more
elaborate graph something more.
Walk with you shall be easily
end up with a graph
along the lines of this.
It should look
something like this.
Now.
We are going to be creating
a parameter in Tableau.
So basically the scenario I
am trying to create is a what
if scenario like I
had mentioned before so
I'm going to say for example,
what if the sales
has been hiked up by 3%
Now this detail is not given
readily to me in my data set.
Set so I obviously have
to create a parameter for it.
So basically this is a parameter
which I'm going to be using
in a calculated field to create
a calculated field
you go to analysis
and create calculated field
or alternatively you
could also go to this
down arrow key near dimensions.
And the first option you get
is a calculated field now
before we make
the calculated field,
let us create the Our meter
that we are going to be using
in the calculated field.
So the second option
from that was create parameter,
so I have my dialog box.
So I'm going to go ahead
and name this like if
because it's an if if scenario
if sales parameter,
I'm going to move
the data type to integer
from the drop-down
menu current value.
I am going to keep
as zero trust me.
There's a reason why I'm doing
this just pray for matters.
Is automatic now
I'm going to go ahead
and select the range minimum.
I'm going to be keeping
0 maximum the default as
hundred and step size.
I am going to keep
as to so with that.
I have created my parameter.
It's going to be an integer
type parameter ranging
from zero to hundred now,
as you can see in the bottom
here in the parameters set
you can see and
if sales parameter the one
that we have just created.
Remember that I had told you
that you have to use
your parameter and tight
to your view in some way.
And the first way that
I have told you was
to use your parameter
in your calculation.
And that is exactly
what we are going to do.
Now in the scenario.
We want to use our parameter
and a few Tableau functions
to create a calculated field
to add to our graph
and then we are going to see
its effect on our data.
So we're going to go
ahead and and create
a calculated field now.
I'm going to name it
the same as my parameter.
Except for I'm going
to name it calculation
and here I am going
to be throwing in a formula
which if you want to know more
about you can go ahead
and check a tutorial
that we have
on functions in Tableau.
So you understand
what these functions do
basically So I'm putting
in this formula here
and at the bottom it says
calculation is valid now.
I cannot stress enough.
So I'm going to say it
one more time in this video.
I've mentioned it
many times in many videos
and lives before that.
This is something which shows
that Tableau is a really really
smart software if suppose.
I remove a parentheses
from here in the bottom.
It is going to show
the calculation contains errors,
and if you hit the arrow button
here it's going to tell you.
What error it is this way?
It prevents you from making
mistakes right from the get-go.
You do not have to go much
further in your process.
This is going to be a small
process for demo purposes.
But usually Tableau is used
in an industry level and there
once you have gone much further
in your procedure coming back
and correcting mistakes
will be Troublesome.
So I'm going to hit okay
and in your measure section
you can See your
if sales calculation is here.
Now.
What I want you to do is notice
how your calculation
the parameter
that we created
is going to interact
with your sales measure
in the segments
that follow next.
What we're going to do is
parameter control now coming
back to the Tableau main menu,
as I had just mentioned you
can see your calculation field
in the measures pain
and your parameter in Parameter
spin this is your data window.
So I'm going to click on this
and click on the option show
parameter control on the right.
You see this particular option,
which is your
if sales parameter,
this is your parameter control
currently it is there
in this slider form giving you
your range is zero to a hundred
which we had selected.
You can always go ahead
and change it from Slider form
to your type and format.
ERM I prefer the slider form
over the type and form
it's easier to operate for me.
You can go ahead
and make a choice.
This is the top right
of your view.
And this is where by default
your parameter control filter
is always displayed now,
I'm not going to show you
how it is used right now
in the next segment.
You will see what its use is.
So finally we are going
to be using Tableau parameters
in our visualization.
This is the part most of you
might already be waiting for.
Okay.
So starting out.
What I want to do is
I want to switch this
for measure values and I
am only going to be keeping.
my f sales and the regular sales
which we had made
the graph with before.
Okay.
Now I have my calculated field
if sales right here.
And so I have my sales
also right here.
Okay now because on
your right you can see
your parameter control is
at 0 you might be able
to see just one graph.
But as we start moving
the slider up suppose I keep
mine at 30 you instantly get
two of your graphs.
One of them is your
if calculation your
if sales calculation,
which is your calculation grown
by 3% your sales grown
by 3% Right and the other one
is your regular sales.
You see the difference
once you click on your parameter
control and set it to any number
which is visible.
Like I would suggest
go above 10.
I have set it as 30 you
can see your dual axis graph.
You can go ahead
and change the color
if you want to but I'd like
to keep them in blue itself
because I think
it's pretty visible.
But you can always go ahead
and change the colors.
If you like.
Now these lines represent
the running values
of sales from your data set
and the calculated
sales simultaneously
and you successfully
Incorporated your parameter
in your visualization
along with your control
that you have
on your right and that is
how you create and use
parameters in Tableau now
parameters are Dynamic
and useful elements
for You to add interactivity
and flexibility to
your dashboards and reports.
It is a very versatile tool
and can be used in way more than
what I showed you in this demo.
It can be used in various
calculations sets equally
well now this is one
of the many smart features
that are there in Tableau,
which is emerging as one
of the hottest Trends in
business intelligence in 2019.
And also if I might add it
is one of the easiest First Data
visualization tools to learn one
of the most interactive
and smart software's
and if you look
at Google Trends,
it seems like there
can be no better time
than right now to get certified
in Tableau to start
learning Tableau in
a world with generates
and consumes 2.5 quintillion
bytes of data a day.
Organizations are bound
to look for new methods
to And combine data
in order to obtain Optimum
efficiency one such method
of combining data
is data blending in Tableau.
Hi all this is a pastor
from Ed Eureka.
And in this module,
we're going to talk all
about data blending.
But before we begin,
let's discuss our
agenda for today.
So first of all,
we're going to talk a little bit
about the objective
of data blending then
we're going to talk
about what data
blending essentially is
and how it works in.
Whoa, then you're going
to discuss a concept
called joining and see
how is it different
from data blending.
Then we're going to see
how can you do this?
It's going to be a very
short demo a few simple steps.
And finally we're going
to discuss a few limitations
in this process.
So without Much Ado,
let's get straight
to the module.
So what is the objective
of data blending in Tableau?
Why do we need
data blending now?
Let's suppose you have
transactional data
stored in Salesforce.
I'm quarter data stored
in an Excel workbook the data
you want to combine stored
in different databases
and the granularity
of the data captured
in each table is different.
So in such a case,
you use data blending
now data blending
could be very useful
under a few conditions.
Like you want to combine data
from different databases
that are not supported by
cross database joints now
cross database joins.
Do not support connections.
Two cubes take Oracle
essbase for instance
or some extract only connections
take Google analytics
as your example in this case
set up individual data sources
for the data you want to analyze
and then use data blending
to combine the data sources
on a single sheet.
Next is when you have data
at different levels of detail.
Now sometimes one data set
captures data using greater
or lesser granularity
than the other data.
For example suppose you
are analyzing transactional data
and quota data.
Now your transactional data
might capture all transactions.
However, what our data
might aggregate transactions
at a quarter level
because the transactional
values are captured
at a different level of detail
in each data set you should use
data blending to combine
data now third case is
when you have a lot of data
typically joins are recommended
for combining data
from the same database.
So join is basically
another method of
data merging in Tableau.
We shall discuss it
in depth in the later parts
of this module now joins
a handled by the database
which allows joints to leverage
some of the databases
native capabilities.
However, if you're working
with large sets joins can put
a strain on the database
and significantly affect
performance in such
a case data blending might be
of great use to you
because Tableau handles
combining the data
after the data is a aggregated
there is less data to combine
when there is less data
to combine generally
performance improves.
And finally you
can use data blending
when your data
needs some cleaning
if your tables do not match up
with each other correctly
after join setup data sources
for each table make
any necessary customizations,
which basically will include
renaming columns changing
column data types creating
groups and so on and so forth,
then you can use
data blending together.
Combine the data now
that we know
when to use data blending,
let's find out what
data blending actually means.
So data blending is
a method to combine data
that supplements a table
of data from one data source to
another data source for people
who use SQL it is basically an
advanced version of your left.
Join now, what is a join
and how is it different
from blending data in Tableau
now data blending skin?
Emulates a traditional left join
which I had mentioned
a few seconds ago.
The main difference
between the two is
when the join is performed
with respect to aggregation.
Now when you use a left join
to combine data a query is sent
to the database
where the join is performed
using a left join returns
all rows from the left table
and any Rose
from the right table
that has a corresponding
row match in the left table.
The results are then sent
to Tableau to be aggregated.
For example suppose you have
the following tables
if the common columns
are user ID a left join
takes all the data
from the left table as
well as all the data
from the right table
because each row now has
a corresponding row to match.
Now, how is it different
from data blending now,
when you use data blending
to combine data a query is sent
to the database
for each data source
that you're using the results
of these queries
including the aggregated data.
A sent back to and combined by
Tableau now take for instance.
You have the following tables
again the same tables
if the linking Fields again
our user ID on each table
blending your data takes all
the data from the left table
and supplements the left
table with the data
from the right table
The View uses all the rows
from the primary data
source the left table
and the aggregated rose
from the secondary data source,
which is the right.
Able and it is done
based on the dimension
of the linking Fields.
If there are multiple values
for Rose and asterisk
is shown measure values
are aggregated based on
how the field is aggregated
and the view in this case
not all values can be a part
of the resulting table
because of two reasons.
First a row in the left
table does not have
a corresponding row match
in the right table
as indicated by the null value
and second there are
multiple Values in the rows
in the right table
as indicated by the asterisk
or the star sign now suppose you
have the same tables as before
but the secondary data source
contains a new field
called finds again.
If the linking feels
our user ID blending,
your data takes all the data
from the left table
and supplements it with the data
from the right table.
In this case.
You see the same null value
and In the previous example
in addition to two things now
because the fines field is
a measure you see the row values
for the finds field aggregated
before the data in the right
table is combined as
for the previous example a row
in the left table does not have
a corresponding Row
for the fines
and that is why it is indicated
by the second null value.
Now, how can you
blend your data now?
You can use data blending
when you have data
in separate data sources
that you want
to analyze together
on a single Sheet example.
I'm going to show
you now demonstrates
how to blend your data
from two different sources now
for this I'll be moving on
to my Tableau desktop
and here I'm going
to be using two data sources
name the sample Superstore,
which is already included in
the sample data sets of Tableau
and the sample coffee chain,
which is another very
easily available data set
for Tableau online.
So first, I have already
loaded the sample coffee chain
to Tableau and now
here is its metadata.
We see profit margin sales
cogs total expenses marketing
inventory budget profit.
Margin Budget Sales, etc.
Etc.
And this is all in an MS
access database file here.
You can see all
the various tables and joints
that are there in this query
right here next step.
Is adding a
secondary data source.
So what we're going to do
is we're going to add
a secondary data source
named Sample Superstore
by again following the steps.
It's pretty simple.
Actually.
All you have to do is click
on this add button
and you will find
the data set right there.
You can search
for the data set here.
And that's it here.
We have both of our data sets
now blending your data.
These are both our metadata
has we're going to go
to our Sheet now
what we can do is we
can integrate the data
from both of the sources based
on a common Dimension.
So when I select this state,
so what I'm going to do,
I'm going to select
the sample Superstore go
to the profit ratio
put it in my columns,
then I'm going to Rose.
I'm going to be selecting state.
Putting it in the row shelf.
Then I'm going
to select this chart.
Let's try the Gantt bar.
Nope automatic it is.
Now if I go
to my coffee chain query and
if I look at my state Dimension,
what do I see here?
This is a small chain like image
that is appearing
near the state Dimension.
This basically indicates
that the common Dimension
between the two data source is
something called the state
if I open my other
data sources as well,
which is the sample Superstore.
This is my common Dimension
and the chart here
basically shows Shows
how the profit ratio
varies from each state
in both the superstore
and the coffee chain shops.
And that was it
for our Tableau desktop.
Let me go back
to my presentation
where we can go ahead now
limitations of data blending.
Now, what are the constraints
that apply to this method?
First of all blending with
non-additive Aggregates now,
there are some blending
limitations around
the non-additive Aggregates
such as Count the median
and raw SQL aggregate
when you blend on a field
with a high level of granularity
suppose date instead of your let
's say queries can be slow down.
So basically the speed
of the query gets compromised
now values appear
after blending the data sources
now null values can sometimes
appear in place of the data
you want in The View
when you're using data blending
and this happens
because of a few reasons,
it can be so that the second
data source does not contain
values corresponding
to the primary data source,
or the data types of the fields.
You are blending are
on different levels of detail
or the value in the primary
and secondary data sources
use different casing
it can be anything
but the null values sometimes
after data blending appear
in place of the data
you want to view
and finally sorting by
feels is unavailable
for data Blended measures,
but despite that data blending.
Is a whole new approach
to merging of your data.
It saves you a lot
more time makes
your system way more efficient
and optimizes the data cycle
as a whole a tableau
developer today is one
of the most sought-after
job roles in the bi industry.
So what does it take
to become a tableau developer?
Well, you have all come
to the right place.
Hi all I'm a pastor from Eddie.
Erica and in this module,
we are going to talk
all things career
when it comes to Tableau.
But before we begin,
let's talk a little bit
about our agenda
for today here first.
We will be talking a little bit
about Tableau followed by
the role of a tableau developer.
Then we shall discuss
the responsibility
and job profile
of Tableau developer later.
We shall explore
the required skills
and abilities for
the same job role.
And finally we're going
to talk a little A bit
about getting certified
in Tableau and improving
other technical skills.
So without Much Ado,
let's get straight
into the module.
So what is Tableau
now tableaus a platform
that focuses on understanding
data and uses its potential
in business strategy.
It's a platform
that comprises of creating
dashboard reports visualizations
and deriving insights
and feedback to improve
on larger systems.
The next logical question is
who is eight.
Tableau developer now a tableau
developer create solutions
for data visualization to
enhance the business processes.
This job comprises
of various tasks such as
working with developers
creating Tableau reports
creating bi visualization
and participating
in feedback sessions
to improve systems.
Now this job is perfect
for people who work
well as a part of a team and
who have problem solving skills
that can manage their time
productively to meet deadlines.
Adaptive developer
is usually someone
that is proficient in data
visualization mathematical
reasoning database skills
and extract transform
and load increase
s's pursuing a career
as a tableau developer can mean
many things a few of those.
I want to discuss
with you today.
First of all the connectivity
option you get the reason
why Tableau stands out
amongst all the bi tools is
that there is a wide
range of connectivity.
See options now Tableau
can connect to any data.
You can possibly
think about starting
from spreadsheets to databases
and even big data you
can access warehouses Cloud
applications like Salesforce
and even connect
to Cloud database
like Amazon redshift.
It has a web data connector
and it is used to pull
API directly from web
in order to connect
to any desired data source.
Now, let's talk a little bit
about pursuing a career
as Tableau developer Tableau is
known as the leader in bi tools
and it has been crowned
the best by the it
research giant Gartner Gartner's
magic quadrant mentioned
Tableau for the fifth time
in a row as the best
amongst a blos competitors
like Microsoft sap
and click in addition
to having a great demand
for Tableau experts.
There are always rewards
to offer if you browse
through the job portals
like indeed and AngelList.
You can find plenty of job
postings Tableau professionals.
Get the best of salaries
in the mighty come bi industry
with an average of
91 thousand dollars per annum.
There are tons
of jobs available,
which require Tableau
as images skill set.
Now, let's take a look
at the responsibilities
that come with this lucrative
job profile now Tableau
developers responsibilities vary
depending on the type
of organization they
work for Here are
a few job descriptions
that I have picked
out by major companies
for a tableau developer.
This is the one by cognizant
which says they want somebody
with industry experience
with Hanson in design
and development of
Tableau visualization solutions.
They want somebody
with creation of users
groups projects workbooks
and appropriate permission sets
for Tableau server log ons
and Security checks
apart from this.
They also want
the Channel to have
strong data warehousing and
business intelligence skills.
Next we have a job
description by Bosh.
They've kept it pretty simple.
They want an engineering
graduate with at least
two to three years of experience
on Tableau minimum a year
or two of experience on Tableau
professional or analyst
software suite experience
and desktop and server
architecture creation
configuration and deployment
of Tableau servers
in visualization.
Jan and Publishing
authorization Concepts along
with some good communication
and analytical skills apart
from which they want somebody
with experience in working
with multiple data sources
and handling large
volumes of data.
Next.
We have a job profile
by Tech Mahindra.
They have kept it
pretty simple as well.
They want somebody
with a strong understanding
of advanced Tableau features
including calculated
Fields parameters
table calculations Joy.
coins and dashboard action
the shoes they expect
you to fill generates
Tableau dashboards
with quick context
Global filters parameters
and calculated fields
on Tableau 10 point x
reports apart from which from
what I see they need somebody
with strong structured
query language skills
in building complex
queries triggers indexes
involving multiple tables
from different database schemas,
but all of these job
descriptions basically boil,
Don't do these major points
a tableau developer
is responsible for creating
Technical Solutions,
which basically means
the primary objective
of this developer is to create
a solution which matches
the needs of the business.
This can be done by finding
Innovative resolutions and
translating their requirements.
Another responsibility is
working with data storage tools,
which preserve data
within organizations.
This is also known as
online analytical processing.
A processing or olap apart
from which they need to conduct
tests for which they
develop database queries
and conduct unit
test to troubleshoot
and analyze the issues
that arise this process
is an ongoing part
of the development
that occurs continuously
throughout the project.
They also need
to enhance systems,
which is a crucial
part of the job.
It means they have to evaluate
and improve existing systems.
This also includes collaborating
with other teams
within the business.
To incorporate new
systems to streamline
company process and workflow.
They also need to create
technical documentation
for completed projects
to communicate with
senior staff members
and colleagues within
the organization for reference.
And finally they need to use
bi Technologies structured
query language data analysis
and ETL tools for storytelling
and forecasting of data.
Tableau represents data,
like no other tool with unique
features like forecasting
and storytelling one can even
connect to the data personally
and understand the depth
of the analysis having said
that let's move on to look
at certain skills
that are required to fulfill
these responsibilities
of a tableau developer.
Now the required abilities to
become a tableau developer are
as follows a tableau developer
should have a bachelor's
degree in business.
Computer science
or any similar field
they require experience
in the whole life cycle
development of applications
at an Enterprise level.
They need to have proficiency
with structured query language
has a large data sets.
They should have
excellent analytical skills
as they are needed to analyze
the requirements of a client
or a business.
This role also demands to work
with software from the beginning
till the end of the project.
So they need to solve
any issue that occurs
during the development.
Onstage apart from that
this profile requires
the ability to create
innovative solutions to problems
with in business.
They need to be self-motivated
for finding Solutions
and improvements to system
during the phase
of the customers
testing and prototyping.
They need to maintain
strong attention to detail
for spotting errors in data
or coding having
a knowledge of microstrategy
and data architecture
is a set bonus
and a little efficiency in
written and oral communication.
Some skills never harmed
anybody now the skill set
that I have mentioned
is enough to make yourself fit
for the position
of a tableau developer.
There are many opportunities
available in the it
market for candidates
who acquire the skills
that I have just mentioned
but the road to acquiring
these skills is a long one
and we added Eureka want
to help you out with this here.
You can learn at your own pace
and take your time
to make yourself
industry ready in tableau.
this program provides structure
and guidance and is curated
specially by industry experts
which covers extensive Concepts
such as data blending
creation of charts
and level of detail
expressions using versions
of Tableau such as Tableau
desktop Tableau public
and Tableau reader this
also covers integration
of Tableau with our and
Big Data having said
that let's discuss a little bit
about the future of Tableau now
the reason why tableaus,
Hands out amongst all
the bi tools is
that there is a wide range
of connectivity options Tableau
can connect to any data
that you can possibly
think about starting
from spreadsheets databases
and even big data you
can access warehouses Cloud
applications like Salesforce
and even connect to Cloud
databases like Amazon redshift.
It has a web data connector
and can be used to pull
API directly from web
in order to connect
with desired data source apart
from that in this world.
Of prevailing Big
Data many organizations
that store wangle
and analyze data choose
Hadoop as their platform
of choice Tableau authorizers
businesses to easily
and quickly identify
valuable data in their
expansive Hadoop data sets
and removes the need
for its users to have
the knowledge of query languages
that makes engaging
with big data more feasible
for stakeholders,
but now natural language
processing and machine learning
enabled data are
two things that tableau.
Is focusing on it is molding
itself with new technologies
to enable futuristic
approaches to view data.
It is going to launch
a hybrid data connectivity
for cloud and with Tableau.
Another Advantage would be
a new life query agent
that will act as a tunnel
to on-premises data,
which will obviously expand
the caliber of Tableau.
So go ahead and get
started with tableau.
So before we start to go
through the different questions,
you can encounter
during table related interviews.
Let me just sort
of like, you know,
tell you the background
whether you have made
the right choice by choosing
to learn Tableau or not.
So here in
this particular slide,
you can see the future
of Tableau as a software
this particular chart,
which you can see is known
as Gartner's magic quadrant.
This was published
on February 2015.
They have published
a recent one.
in February, 2016 and Like
previous magic quadrant stab
you again is the leader
in terms of ability
to execute its again
in the leaders quadrant.
It has come down a little bit in
terms of the Visionary aspects.
I don't know why maybe you know,
I saw Microsoft was right here
if you see the 2016 slide
but still table is very much
the top contender in terms
of ability to execute its
at the highest position.
So the future of travel
definitely is very bright
for the fourth consecutive.
It has been way ahead
of its competitors in terms
of ability to execute and in
terms of completeness of vision.
Again, Tableau is
a strong Contender.
It's lying right here
in the leaders category.
These are challenges.
These are Niche plus these are
Visionaries challenges are those
who have good ability
to execute but,
you know the different variety
of work which you can do through
these software's are limited.
So they have specific
Focus Visionaries are
those Those which have
long-term good prospects,
but it's difficult
for you to execute your work
in those players who are lying
in this Visionary box leaders
on the other hand.
They can perform a variety
of tasks for you and you can do
those Works relatively easier
and Tableau is lying right here
at the top of the stack in terms
of ability to execute job Trends
in terms of job Trends.
Tableau has shown
very good progress.
So here you can see the Of jobs,
which are related to table you
have increased exponentially.
So, this is January 2015.
I think this
might be January 2016.
You can see the demand
for tablet professionals
have grown considerably
and the national salary trend
for Tableau again shows
that that the salary trend
for Tableau is increasing.
So it's worth our time
and effort to learn Tableau and
work in this particular field.
One thing which I can't Tell you
from my perspective being
a tableau developer myself.
I can tell you
the job satisfaction level
is going to be high
because it's fun
to work in Tableau.
So you get constant feedback.
So from job
satisfaction perspective,
I can tell you you
will get instant feedback
while working with Tableau.
So everything is visual
you all the components.
You invoke all the components
you work with the output
which you get everything is
right in front of you.
So, you know,
it's pretty exciting
to work on Tableau as
far as I'm concerned
I can vouch for that
and some major companies
which are using Tableau
include Cisco Google Yahoo.
LinkedIn Facebook YouTube
as you can see the list here.
Okay.
So I was telling you
about major companies
which are using Tableau
and you can see you know, many
big players are using Tableau.
I've worked across multiple
multinational companies many
of them were Fortune 500 and
many of them were using Tableau.
So I work in John Deere.
I worked in Rio Tinto
all of them a Fortune
500 companies all of them.
Them are using Tableau.
So industry adoption
is very high.
And these are two top Contender
when it comes
to visual analytics.
We have Tableau
and we have click View
and here is a comparison
between them the strong suit
of Click view versus Tableau.
So both can analyze
Big Data Tableau
as you can see I can I
unless billions of data analyzed
millions of data ETL tools
are available in qlikview.
ETL tools are not available
in Telugu a so-so.
That's one constraint
which you will face
into a blue and you have to do
your data preparation most of it
outside of taboos environment.
So if you are well aware
of any database if you are
well aware of SQL coding
if you are well aware
of Excel or access
if you can manipulate your data
well through some other tool
then using Tableau.
You can visualize
that data click view
on the other hand offers
you some ETL tools
so you can manipulate your data
within click View.
As well click
view versus Tableau.
Another difference is
qlikview is very technical.
Okay, it's much more complex
as compared to Tableau Tableau
on the other hand is very
intuitive very user-friendly
and to work with Tableau to pick
up Tableau to learn
Tableau is much easier
as compared to click View
and once you have you know,
so while working
with qlikview dashboards,
you have to keep your
and objective in mind
you have to do all
those preparation going back
and changing your We
take extra effort
Tableau on the other hand
provides you the capability
of rapid-fire analysis.
Okay, so you can just push in
your data within chair blue.
You can slice and dice the data
you can pivot this data.
You can experiment
with different kind
of visualization.
So you can work on the Fly very
quickly at any point of time.
If you want to go back
and change some component
of your visualization.
You can do that very easily
that is a challenge
with click view.
Okay.
So if you have to build
that context you have to do
thorough Planning these kind
of constraints are
there with click view
if you are looking for a very
easy to work software.
Tableau is the tool for you.
Now, let's get
into the Crux of matter.
We are going to pick up
certain interview questions,
which we have framed for you.
Some of these questions
has been asked
and raised by some of you and we
have tried to answer them here.
So the first question why
is there a need to go
for Tableau in spite of having
huge number of Open Source
and less costly
visualization tools?
The some of the reasons
which I have mentioned
this this particular question
has been asked by one of you.
Okay, and this question
actually makes sense.
Tableau has a
lot of competitors.
Okay and just guys just
give me one quick second
into for that matter.
So the answer is first
of all Tableau is very
very easy to use dashboards
are simple to build even
for a newest somebody
who has never really worked
with the visualization tool
can learn Tableau easily.
That is one big reason.
Also, even for an end user
from an end user perspective
Tableau is very easy to use
so they have all the filtering
capabilities parameters all
at their disposal.
You can provide your end users
with these features
and they can play around
with the dashboard you
have created for them.
So adoption from a developer's
perspective and from
an end users perspective.
Both is very
high rapid visualization
drag-and-drop feature feed.
So Tableau has this drag
and drop feature
which you can use
for creating visualizations.
Rapidly, even very
very complex dashboard
to not take months to create
which they usually do in some
if you're going to try
some other visualization tool
within Tableau even
complex dashboard.
You can create
in matter of few days.
Okay, not even a week.
I'll say so it's
just drag and drop
and you have to be aware
of some Advanced feature
from time to time
you have to use them.
But mostly it's just
the basic feature
which you are going to require
most of the times now the charts
which Tableau provides you are
very visually appealing Okay.
Tableau is based on best
practices of visualization.
It doesn't provide you 3D charts
3D charts are not good.
Okay.
So this has been proven
through different research
that 3D charts
and other those fancy charts.
They are not good
for visualization simple
and Visually appealing
charts are best means
to present your data
and Tableau is based
on best visualization practices.
It allows you to create visually
appealing charts with vibrant
automatically generated colors.
That is another reason
Tableau is very intuitive.
You can customize you
can modify your visualization
to a great extent and you know,
once you practice Tableau enough
and I'm not talking about,
you know months
and months of practice.
I'm just talking about chocolate
several weeks of practice.
If you work like,
you know, do three four
projects in Tableau,
it will become very
very intuitive to you
because all the components
which you utilize to modify a
chart are right in front of you
so we have those shelves
and cards when you need to drag
and drop your Your values
if you want to show Legends,
they are right in front of you,
you know how to do that.
And if you want to change
the color scheme the option
of changing the color scheme
is right in front of you
so table is very intuitive.
I have tried to use click View
and it was not
that intuitive at all.
In fact, if I compare
it with Tableau,
it doesn't stand anywhere
in terms of ease of use
and intuitive -
also Tableau uses Excel
like formulas and it's
easy transition for many
because many people work on.
Miss Excel and those of you
who have already worked
on Ms. Excel and since you know,
I'm assuming all of you
have learnt a blue
so it will be an easy
transition for you
many of these formulas follow
the same syntax within Tableau
and Excel for example,
if else formula the syntax
is more or less similar.
We have date the formula all
of them are using Excel
as well as Tableau.
So easy transition,
then we have
a active user Community.
Now, this is one
feature of Tableau,
which is not very commonly
mentioned, but I am going
to mention it here.
Okay.
So as a developer, you know,
if you have worked
on some other platforms
some other programming language,
you may realize
that from time to time
we have to depend
on other users user Community.
We have to Google our answer.
Okay, we cannot just
remember all the keywords
all the technicalities
of the software ourself either.
We have to keep
like a thick reference book
or reference manual
or we Google our answers up.
So there is a need
that your software should be
supported by an Was a community
and Tableau serves that feature,
you know W has that feature.
So there's a very active user
Community which supports table.
Umm.
Okay, then Tableau has
in memory of bi platform
enabling High scalable
and Rapid visualization.
Tableau is pretty fast.
If you are working
with large chunk
of data table is not going
to disappoint you I mean,
of course if you're working
with billions of rows of data,
you have to optimize the
performance of your dashboard.
The response will be relatively
slower then when you are working
with smaller did Yeah,
but just compare
Excel and Tableau.
There is no comparison Tableau
just doesn't compact you data.
It actually handles
the data much more faster.
Okay ability to apply filters
and date range on the Fly.
Of course, you
can apply filters.
You can filter out date range.
You can show those filters right
on top of your dashboard
and your end users
can play around
with those filters as well.
Not only that you
can click on one portion
of your chart and filter
down other charts as well.
If you have made
a dashboard there
six seven different charts,
you can click
on one particular chart
or the charts will filter
down automatically
if you have enabled
those features and Tableau works
on user feedback.
Now again, this particular
benefit of using Tableau
is not mentioned commonly.
But what I have seen
is have been using Tableau
for quite some time now
and I started using it way back
like I think
from version 5 or 6.
I do not remember exactly,
but it was lacking a lot
of features back then and users
were constantly providing.
Feedback that this particular
feature should be available that
feature should be available.
In fact, we got
this survey from Chapel
where we provided our feedback
and several of those feedback
which we provided.
We saw that in action
after a few versions.
So now Tableau has
the integrating feature
with our Tableau provides
floating filters, you know,
floating containers earlier.
These features were
not available but Tableau works
on user feedback
and incoming few years.
We may see most
of the desired feature are
We'll interview right now.
There are some limitations
as I mentioned like
ETL tool is not there.
Who knows we may see
some ETL tool with interview
itself in couple
of years from now.
In fact Tableau
has started providing
some data manipulation feature
in the latest versions.
So for instance here,
these are different marks
and these are different
cards and shelves
which you are using to control
your chart to further modify
and customize your chart.
Okay.
They're right here
in front of your eyes in.
Excel you probably
have to right click
and go to certain particular,
you know areas
of the pop-up window,
but here these components
are right in front of you
and you can just click here.
You can modify the text.
You can set the alignment all
these things you can do right
in front of yourself.
So it's pretty intuitive.
All right, let's move
on to the second question,
which we have
how to optimize the reports
for better performance here.
I have put down
certain pointers.
This is not a limited list.
This is not a complete
list as such.
Okay, as you keep
working with Tableau,
you will realize
that there are many more options
which you can employ to fastin,
you know, improve the
performance of your dashboards.
So a recent test
which was run side-by-side
on two different machines
running the same data pull
from Tableau server
Internet Explorer 7,
return results in 11 seconds
Firefox return results
in 3 seconds.
So the browser
which you are using
that makes a difference, okay?
So when you are working
with Tableau server,
it's preferable to tell
your users to use
the latest browser,
which is the fastest one.
Okay, so it does make
a difference second tip
which I would like
to share with you
if complex calculations
are needed in Tableau consider
creating a dbms view
that does the calculation
the database server is
usually more powerful
than the desktop.
Okay.
So if you are going
to filter your data, okay,
if you're going to create some
aggregation you can do that.
Within your dbms as
much as possible.
Are you aware
of granularity of data?
Okay.
So keep in mind
the granularity of your data.
If you feel
that you are not going to use
data at a very granular level
at a very small level.
If you are always going to use
it at an aggregate level.
Go ahead aggregate your data
within dbms itself and then pull
that with interval.
Oh, it's always going to help.
Okay, so tell you doesn't have
to do all that hard work
of aggregating your data.
Let's see if I
can give some examples.
Example, let's say you have
daily transaction it
or maybe per transaction data.
We are collecting data
for a superstar.
Let's say we have Walmart
and we are collecting
data for Walmart.
We have data
for each transaction,
but the analysis which we
are doing that is happening only
at a product level
or maybe at a daily level
or at a monthly level.
Okay, you are never going to go
to particular transaction level
and analyze the data,
you know that already.
Okay.
So what you should do is
you should I would summarize
your data at dbms level and then
pull that data Within Chapter.
It's going to help does
that make sense and consider
the use of pre-computer
aggregated summary tables
when large data
set are required.
Typically when summary summarize
when used okay
again, same point,
you have to sort of
like keep in mind
the granularity of your data.
If you are working
on calculated fields,
which are not going
to change frequently,
maybe row-level calculation
some if-else calculation
which are doing okay.
You can do that right
within the BMS
if you are aggregating your data
summarizing your data do
that outside of Tableau.
It's going to
increase your speed.
Of course, these points
are applicable only
when you're working with
large data sets may be Beyond,
you know, 10 million rows
or something then
it's going to improve
your performance drastically.
Otherwise if the number
of rows you are working
with is less than 1 million
then you know tab you
will be able to handle
that seamlessly.
It's not going to be
a problem at all.
Also, you should turn
off automatic updates.
Whenever possible so,
you know Tableau
doesn't automatically updates
your data, you know,
instead of correcting
making a live connection
make an extract updated
manually whenever possible.
If you do not want
Auto refresh or the data
that's going to speed
in of the things.
So next Point use
as few data sources
as needed to achieve
your analysis and remove
any unused data source,
we have this tendency,
you know of connecting
to different data sources
as an analyst
I can say this I do it
from time to time.
I've seen other people doing it,
even if they do not need
a particular data source still
they make a connection to it.
Okay, because they
might need it in future
if you're working
with big data do not do that.
Okay connect to only
those data sources
which you are going through use
which you are sure you're going
to use and use extracts.
So when you extract the data,
you will actually get additional
features like, you know,
you can use certain formulas.
Tableau is going
to optimize your data
to improve its performance.
So use of extract is going
to increase the performance
of your dashboard
when filtering try to avoid
the exclude option.
So when you're
filtering the data,
there is this exclude option.
If you use that Tableau
will perform a bit slow.
So went exclude option
is used Tableau will scan
all the selected data and then
it will exclude it selectively.
Okay, so try to avoid
the exclude option rather
use the include option.
And use Boolean calculation
whenever possible with Alias.
Okay.
So how to do
that how to use Boolean
calculation whenever possible.
Okay, so I'll just
repeat what I said.
So couple of tips
which I was sharing you can
create an extract of your data
that's going to speed
in up your calculations
because Tableau optimizes
its extract to make
the dashboard perform better.
So always create an extract
whenever possible you
can refresh an extract.
So refreshing your data
if you're worried
about refreshing your data,
whether it's going
to work or not.
That extracting it
it's going to work.
You can refresh
the extract as well
and when filtering try
to avoid the exclude option
because when you use
the exclude option Tableau
will scan all the selected data
and it's going to slow down
the calculations a bit.
Okay slow down the process
a bit rather try to use
the include options.
All these points are applicable
when you are working
with big data set and use
Boolean calculations whenever
possible with Alias.
So for example,
if you are using an if
else formula, okay,
if you are using
an if else formula,
you're trying to
let's say I analyze
if let's say if the age
if age is less than 25 years
or if age is less than 18 say
not a result else say adult.
Okay, if that is the formulae
trying to build just build
a simple formula age less
than 18 you will get results
in form of true and false.
Okay, and then use Alias
to rename true as not adult
and Falls as adult.
Does that make sense?
Let me just quickly show
that to you through an example
how that works.
Sure.
Let's say we have fixed salary
of different employees.
Okay, so we have
different employees
and their fixed salaries.
So some employees
are getting above 10 lakhs.
Some employees are getting
less than 10 lakhs.
All right,
let's proceed further.
There are other ways
in which you can optimize
your formula use else
if rather than nested else
if in your Logical statement
here is a Formula you can see
if region is east
and customer segment is equal
to Consumer then call
it East consumer else.
Now.
This is the start of another
if statement hence
to and statement here.
Are you able to recognize this?
So one if statement another
if statement this is nested
if one statement
within another okay
this one Be slow.
You can write it like this
instead of giving this space
between else and F
and making it nested.
Just give the next option
as else F. Okay,
check the second criteria here.
We have just one
if statement there
for just one in statement.
This will be much faster.
So these are some ways
in which you can optimize
your if-else statement.
Let's say you do not need
the details of time stamp level.
Okay.
So in that case use today
do not use now for smaller data
set more Less you can use today
or now depending
on your preference.
I mean, it doesn't really matter
but when it comes
to large data set,
if you're performing a lot
of time related calculation
based on today's date,
you do not need time
stamp level details
don't use now use today.
Okay, so that will restrict
the scope of calculation.
It will be faster.
Also, you should logically
optimize your calculation.
So for example here
take use of, you know,
make use of logical calculation
these two statements these two
if statement are going to
produce the Exactly same result
but this one the one
at the bottom will be faster.
Okay.
So what does this statement
do this statement is checking
if the sales is less than 10
then it is categorizing this
categorizing the sale
as bad else if the sales
is between 10 and 30,
then it is saying okay
if sales is greater
than 30 then it's saying great.
No more many people will write
the statement like this
and there's nothing wrong,
but if you want to make
it perform faster,
Then you can just
ignore this statement.
This is the default statement.
Okay, you do not need to check
for this condition check
for first condition
if sales is less than 10,
then it's a bad sale
if sales is greater than equal
to 30 then it's a great sale.
Otherwise, it's like
somewhere in between we
have already on this
is a logical statement, right?
So we make use of logic
and we just skip
this entire check
and that will make
our code run faster.
Next step when using extracts
and custom aggregation
divide the calculation
into multiple part place
the roll level calculation
on one calculated field
and the aggregated calculation
on a second calculated field
and then extract
can optimize the precomputed
row-level calculation.
So for example,
if you are trying
to calculate the sum
of salary of all male employees
or average salary
of male employees
versus average salary
of female employees you can
The formula something like this.
You can type in the formula
something like this.
You can say sum of salary
whatever, you know,
if if gender is equal to mail
and then salary else 0 so
what are you doing here
if gender is male
then you are taking the salary
of the person else
you're taking zero,
you're not taking the salary
of the person then
you are summing it up.
So in one way you are performing
a row level calculation
and then you are aggregating it.
Okay, this tip
this particular tip tells you
if you are doing something
like this break it apart
into two different pieces
create two variables type it
like this this entire thing
will become a new variable.
Let's say we'll call
it male salary then you
will say equal to this will be
the formula of mail salary.
And then you will just
perform a sum of male salary.
So they'll be two variables
if you create two variables
and then take an extract table
is going to perform
the row level calculation
and optimize it
and then aggregations
will be faster remove
any unneeded dimensional measure
from your palate next point.
So we are working on
some particular visualization.
Let's see this one
and what you will see
from time to time.
They'll be something lying
in the detail section,
which you are not using.
Maybe you were experimenting
with your view you try to create
three four different
kind of visualization,
and then you landed
upon the visualization
of your choice.
Okay, but what happened
in turn was there were some
there were some information
which got left behind
in the detail section.
It has happened with me,
you know at from times
to times I go back to some
of my visualization
and I see there is something
lacking in the detail section.
I never intended
to put it there,
but then I was experimenting
with my visualization and bye.
Steak.
I left in a particular unwanted
filled in the details section.
It didn't hamper
my visualization.
It has no effect on
my visualization as such okay,
but it's still just lying
there sitting there making
my dashboard slower.
So in those cases,
it's actually good
to revisit your shelf and see
if there is some extra item,
which is not needed typically
in the detail shelf.
You will find some of
those things just remove them.
Okay, so there are a lot of tips
which I shared with you.
In terms of making
your reports perform
better performance optimization
therefore the tips which I
can give you for example,
if you are making a tabular
structure interview, you know,
you're not really creating
a visualization rather creating
a tabular structure like this.
This is table and people
are supposed to scroll
through this table
and you know look
through the data so we have
this tabular structure.
If you are picking
any of these three,
make sure The scrolling feature
is not taking you very far down
because if your table
is very very big.
It's going to give a you know,
terrible hit to
your performance.
Okay, so just make sure
if you're doing something like
this create a hierarchy enable
plus and minus features,
especially, you know,
people create huge table
when there is
hierarchy in world.
So for example,
Global sales broken up by
Regional seeds broken up
by country level sales, bro.
Open up by state level
sales broken up by
City level state sales.
This will turn out
to be a huge table
and people have to really
scroll down create
a hierarchy let people click
on plus and minus button,
you know expand
or collapse The View
because that we use
going to slow down
new dashboard considerably.
So that's another tip.
I wanted to share.
And then we have
some context filters.
Any one of you is not aware
of context filters
their context filters.
They are also used
for optimizing the performance
goes from X to X.
So there are
many different features.
You can use to optimize the
performance of your dashboard.
Okay, third
question canterville,
you create operational report
where data changes every second
and also it looks like the drill
down the table data
that Tableau shows
as picture format
and cannot be refreshed real.
Time I was not able
to comprehend this question
completely but I am assuming
this question actually means
that how you can actually
refresh the data table.
Okay.
So there is a scheduling
n sort of like scheduling tasks
which you can perform
in Tableau that is done
on server side actually.
So here is a link
which I provided you
will get this PPT.
And once you click on this link,
it will give you
step-by-step guidance on
how to schedule reports.
There are some
predefined schedules.
So for example,
Can enable your report
to refresh every 15 minutes
or end of a particular month
or every four hours.
So there are different
predefined timings
in which you can schedule
the refresh of your report.
Let's move on next question
how a real-time Tableau project
will be in an organization
from development to publishing.
Okay.
So it actually differs
from condition to condition,
you know from
situation to situation.
There can be different
kind of tableau.
Projects you can encounter
and I'm just purely answering
this based on my experience.
They can be other
situation as well.
Okay, Tableau project can
either be a migration project
a migration project means
your report was existing
in some other tool.
Maybe it was an Excel report.
Maybe it was
a business objects report
and now you have been given
the task to migrated to table
this this real stuff,
which I'm talking
about, you know,
and you will probably encounter
situation out of these four only
there might be Other situation
which I have not encountered
and you may okay.
So these migration projects
they are easy to work
with and they are profitable.
So by profitable
what I mean to say is people
can people will actually get
to compare your report
versus the older port
and they will be able
to see the difference
and you will get a lot
of praises and I've got
that my colleagues
have got that in past.
So these are like,
you know good projects
to work on Plus.
Amounts will be crystal clear
because you are
migrating something.
All the requirements
have been noted down.
There's a practical application
sitting in front of you
which you have to replicate
in Tableau and make it better,
of course visually appealing
and you know jabu's
fast engine slicing
and dicing ability
all these things you
have to apply these projects
will be easier to handle
and mostly it will
when you lot of accolades
and appreciation.
Then you have brand new projects
brand new projects as in like,
you know, business doesn't know
what it wants you have.
Data sitting in front
of you maybe business
has asked you to create
a particular report,
which was not existing earlier
and you are doing it in Tableau.
This is also a good
thing to work on
and you will in turn learn a lot
of things what hear
what you have to do is
really experiment a lot
with visualization just be
creative think about you know,
what all visualizations you
can show your dashboard.
So again, these kind
of projects are there then we
have ad hoc reporting projects
where you are given exact set
of requirements small.
Projects may be okay
and you have to create
a small Tableau dashboard
in order to fulfill
those requirements.
It may be required
for one-time use.
Maybe it's going to get used
for only one week
and then scrapped.
Okay, so these ad
hoc reporting products
are also very common
across different Enterprises.
These are done in tell you why
because table is very fast and
you can actually create reports
like you do in Excel
pivot tables and you
can actually create reports
that quickly interval.
So a doc reporting is
again a very common tasks
in Then exploratory research
to guide predictive modeling
if you're working on full-scale
predictive modeling projects.
So Tableau is going to be
a very handy tool for you.
I'm from predictive
model modeling background
and I can tell you
that within predictive
modeling there can be lot
of permutation combination.
It would really help
if you know your data best.
Okay interview helps you do
that Tableau gives you output
in a very practical
and I mean the entire feel
of data you will get
while working with them.
Blue, okay, so you will know
in and out of your data.
Once you come
out of the Tableau environment
and slice and dice the data,
you can go to any
level in Tableau.
So exploratory research to guide
predictive modeling is done
quite commonly interview.
So you'll pull your data
you'll create scatter plot.
You'll create some bar charts
and you'll see
how the trend is moving
and then you will
deploy predictive models
where you find
interesting Trends or
where you see there is potential
of predictive modeling to bring
out some good results.
Okay.
Otherwise it's difficult to No,
just go ahead and deploy
predictive modeling
and then come out
with no results and try
some alternate approach.
It's a slow process
and very intensive courses.
So w is going to help you there.
These are different
kind of projects.
You may encounter
and based on the kind of project
you are doing the approach
which you're going
to take will vary
so I will talk
about that briefly in my next
question not this one.
I will come back
to this before that.
Let me try to answer
this particular question.
Now this has some relation
to the question
which I just discussed
the different projects
which happens in Enterprise
and what are considered
to be the components
or or the approach
which you should take to create
the best possible dashboard.
So first thing
involve business people
in dashboard Design This
is a softer aspect.
This is not anything
technical but it hugely
affects the success rate
of your end product.
Okay specially if you
are working on These kind
of projects if you're working
on migration project
or brand new projects.
It's imperative to involve
business people have recurring
meetings with them.
Okay, probably
if your project is going to be
like two or three months
long have recurring
weekly meeting may be okay
or maybe twice a week show
them your progress take
feedback from them
and then keep on working.
They will provide you
the necessary business
context as well.
So my grave Great Migration
project and brand new project
in those cases.
You should involve business
people and seek their feedback
and use an iterative
dashboard design approach
which means build your dashboard
certain components of it show it
to the business
gather their feedback
and then work on it again.
Okay, this scope of success
the chances of success
will be much higher
then allow drill-down
capabilities within dashboard.
You should always
allow drill-down capabilities
within dashboard rather
than creating separate.
For each different condition
rather than creating
distribution of mail.
Let's say we are working
on a HR dashboard
and we want to see how many
male employees are working
across different departments.
How many female
employees are working
across different departments?
And then how many male employees
less than 25 years of working
in different department?
How many male employees
between 25 to 35 were working
in different departments?
So we have all these different
permutation and combination one
may feel tempted.
Added to create, you know,
multiple different histograms.
Maybe for each of
these situation do not do that.
Use the drill down capabilities
of dashboard create a pie chart
for male-female create
a histogram for age distribution
and then create a bar chart
for number of employees working
in each department
show your uses
that they can click
on one section of the pie then
another section of the histogram
and then the bar.
That will Auto filter.
Okay, so they can
choose male employees
between is of 45 to 60.
They can choose female employees
between age of 25 to 35
and they can see the numbers
how many employees are working
in each department these drill
down capabilities of Tableau.
You should utilize
to the fullest.
Also, please include actionable
informations some information
which might be very
relevant and interesting
to you might not be actionable
to your end users.
Okay, you may assure them
that your sales is decreasing.
They might be knowing
it already or even
if they're not knowing it.
It's just going to panic them.
What is actionable information.
Why is the sale decreasing?
Okay, what can probably
be done to improve those sales
where our Still going.
Okay.
What are they doing?
Good.
Okay, so these kind of action
and actionable information
if you show then,
you know the adoption
or the success
of your dashboard will be high
don't include too much.
So don't overburden
your dashboard.
Keep it simple.
Keep it non-cluttered.
If you want create multiple
dashboards in your file.
Okay.
Don't put too much
of information within one single
screen show relevant filters
relevant filters means in Intel.
Ooh, you have
this feature of creating
relevant relevant filters.
I hope you are aware of it.
Right?
So for example,
I can use floor number as
a filter here float number
and these flow
numbers are appearing.
This is showing
me different employees
and which flow they are working
on if I want to see
only those employees
who are working on 4th floor.
I can select this filter
and then we have
this Department's name.
Okay, so we have all
these different departments now
thing is some of the department
they are sitting
on particular for floor.
So for example,
CEO office see you office
is sitting on floor
flow development development
is also sitting on 4th floor.
HR HR is not sitting
on fourth floor,
but still it is showing up
in this filtering keep filtering
option show relevant filters.
Okay.
So what you should do
is click on this drop-down
and Choose this thing
only relevant values.
This is a big confusion point
for and users.
They will try to
choose a combination
which doesn't exist
and then they will get scared
that there is some problem
with the data.
They will come back question
you they'll get frustrated.
So these kind of things happen.
This is this very simple option,
which you should always keep in
mind show only relevant values.
Now if I'm going
to choose fifth floor,
I will see different departments
if I'm going to
choose sixth floor.
I'm going to set
different department.
Um, If I'm going to choose all
see all the Departments
so show relevant filters.
Okay.
This is a good practice keep
an eye open for color blindness.
I was very ignorant
towards this particular fact
till I learned it the hard way.
I created this dashboard.
It got sent across
to multiple different leaders
and the top leader.
He was colorblind.
I didn't knew I was
not careful enough and you know
that dashboard came back to me.
I Add to make
it colorblind proof.
So if your dashboard
is being shared across
multiple different users may be
like you're 50 or a hundred
different user assume
that some of them probably
will be facing this difficulty.
Okay, and just
adopt some technique
through which you can count
the color and brightness.
What are those techniques
you can use Shades instead
of vibrant colors?
Okay.
So if you are creating
some, you know,
if you're creating a bar chart
like this maybe Like this.
Okay.
Try to give shit here.
You can see these are shades
of the same color
two different shades
of almost the same color.
This can be interpreted
by a colorblind person.
Okay, if you if you
give too dark colors
of different shades
may be green or yellow
color blind person may find
it difficult to comprehend.
So just keep an eye open
for color blindness
as well choose
between percentage
and real numeric value.
So from time to time you have
to show values as percent.
Age from time to time you have
to show real numeric values
if we have three
different departments.
Okay, one department
is testing Department,
which have 500
different people working.
Another department
is HR department,
which have 50 people
then we have pmo
project management office,
which have let's say
10 people only.
I want to see.
Bifurcation of male and female
in these three Department.
I want to compare which has
a healthy gender ratio.
What should I show real values
or percentage values
percentage values is
what we should be showing right
because you cannot compare
apples and oranges.
Okay.
So if you say there are
a hundred female in testing
and they are only 40 female
in HR that will present
a very wrong picture
because hundred female
out of 500 in testing
that's like 20 percent
and 40 female out of 15 HR.
That's like 80 percent.
So there's no comparison.
The gender ratio is
probably you know,
the female ratio is
much more higher in HR.
So sure you need
to show percentage values.
So just think carefully
about it the example,
which I gave to you was
pretty apparent in real world.
It might not be that happen.
So you have to Think
of it carefully
whether I should
show percentage value
or numeric value and follow
consistent and differential
color coding is for example,
if you are
building HR dashboard,
you are showing male female
in a pie chart and then
male-female again in some
hundred percent stacked bar.
Try to follow consistent
coding for them.
It should be consistent and also
it should be differential.
So for example
if you are using Yellow
for female then don't use yellow
for managerial employees.
Okay.
So here is an example here.
This is managerial employees.
These are female employees
this code for female.
This is code
for people managers.
Okay, those who have
some team reporting
to them whenever I'm going
to use people manager
in any other dashboard as well.
I'm going to use this color only
whenever I'm going to use
female anywhere else maybe
if I'm going to say,
let's talk about terminations.
You know, how many
female got terminated
how many male
got terminated like that?
So I will try to use
the same color scheme.
So be consistent
and differential show applied
filters for user.
He's okay.
Now what happens is
if your users have too many
different filters and specially
if they are working
across multiple dashboards,
if you have provided them
six seven different dashboards
and somehow you have
linked these dashboards
so there Line Filter
in one area and there,
you know actually looking at the
dashboard in some other place.
It's a good idea
to actually show them
what filters they
have already applied.
How can you do that?
Let me see if I have
an example here.
I'm going to open up an example
which will show you.
Okay, nevermind.
I'll create the
feature here itself.
So let's say I'm applying
a filter on those employees
who are working
on 4th floor and can then those
who are working
in testing and those
who are working
as senior associate.
Okay, if I want, you know,
it's actually a good idea
to show your end user
which filter they
have already applied.
How can you do that?
Maybe let's do it your
I'm going to expand
it a little bit.
I'm just going
to edit the title.
And you can sin.
Okay.
I'm sorry.
Not you.
Let me just with that
some other place.
Let me do it here
so you can say gender gender
is equal to gender
and then you can say let's say
Department name is equal
to D PT is equal to department.
So like this you can create
sort of like labels
here in a better way.
Of course.
I'm doing it very
quickly and then
if people are applying
filter lets somebody apply
the filter on pmo.
They'll be able
to see right here
that the pick the filter
they have applied.
They have chosen
Department to be pmo.
And then let's say they
have chosen female.
So the current filters
the screen which they
are looking at is for female
gender working in pmo.
It's a good idea
to actually enable it.
Yeah, and especially
if you are using action filters,
then you can actually
apply filters across
different dashboards.
Okay, so I can choose female
here and it's actually
going to filter out
some of my dashboard,
you know, some of my charts
in this particular dashboard.
You can enable
those filter features also using
action filters in that case.
It's absolutely imperative
to show these filters.
Okay what filters they
have applied otherwise
people will get confused.
They'll absorb some
other information.
They'll make different.
Predation or affect the might
not even be aware.
They are looking
at only female population.
Okay, they will assume
that it's for all employees.
Okay.
So show applied
filters for user.
He's it's a good idea
and test your dashboard
on different screens.
I would recommend using this
particular feature of Tableau.
Go to dashboard
keep a fixed size.
Okay, do not never ever ever
ever choose this option.
This is Is suicidal.
Yeah, and that's the word
which comes to
my mind automatic.
Never choose this
if I'm going to project this
through a projector
on a you know,
a screen it's going
to look different
if I'm going to transfer it
onto some of you you have
a different screen size.
It's going to look
different in your case.
Some of the charts May blow
up some charts will become
exceedingly small some charts
will become exceedingly big
this option is never a good idea
always go for exactly
or choose like a no
Defined exact size
and also it's good to check it
on different screen specially
if you are going to transfer it
to different users now many
of these tips are applicable
for these two projects.
So for migration projects
and brand new projects,
especially if you have more
than no.3 for users
who are going to use
that particular dashboard,
make sure you
are following those tips,
which I shared with you maybe
for ad hoc reporting
which is going only
to one particular.
Ziggler user or for
exploratory research
which you're going
to do for yourself.
You might not
require those steps.
All right how to do
effective data blending.
This is another question which
we got so for data blending,
of course, I mean,
it's a more complex
Topic in Tableau,
but for Effective data blending,
these are some points
which you need to keep in mind
when you have
data blending done.
Your primary data source
will show up in blue
and secondary data source
will show up in Orange here.
You can see this is secondary.
This is primary.
What if I want
my dashboard to be viewable
on mobile devices.
So in case if you want
your dashboard to be viewable
on mobile devices there
might be some option here.
I have Let's see we have iPad.
We have iPad landscape.
You can also specify exact size.
So here you can Define
exact width and height
and you can optimize the view
for mobile devices.
You can do that and just in case
if I make it large enough,
let's II make it like,
you know if I increase
the height to 900 I'll still
At the scroll bar.
Okay, so I can scroll
through my dashboard.
Only thing is it's not going
to fit on one single screen,
but the aspect ratio
of charts is not going to change
that is one huge Advantage
which I will be getting
if I'm going
to choose automatic.
My aspect ratio will change
this will become broader.
Some will become smaller.
You know, that's going
to become very challenging.
How to connect
to a database okay,
if you want to connect
to a database
when you open up Tableau
or let's say you
have created your dashboard
and you want to connect
to a database here you go
to new data source.
If you have access
to Tableau Data server,
you can go to the section.
So some Enterprises
what they do,
they'll build their own
Tableau Data server,
and they'll provide you access
to that data server
and you can connect
from here else.
You can choose new data source.
I am using The public right now,
so I do not have option
of connecting to a database
but you will see those options
available available here.
Okay, and once you
click on the option,
let's say you want
to connect your Ms.
SQL database through Tableau.
Okay.
So let me show you
how it looks like.
So if you want to connect
from tablet Ms. SQL,
first screen,
which you will see will be
something like this.
It's kind of hazy.
I'm not sure
if you are able to see it.
Well, so you will get all
these different options
when you are working
with Tableau desktop,
you'll get all
these different options.
You can choose anyone as
per your preference.
If you want to connect
to Oracle database,
let's say you want to connect
to Ms. SQL database.
Okay, then you
will see a screen.
Like this.
Yeah use Windows authentication
or username password
you can provide
and then you will see a screen
where you can actually
write in the SQL code.
If you want to you
will see all the tables
which are available.
And you can also write
in a custom SQL if you want
to something like this.
So let's say you have
a sequel Builder.
It's a you have MS
you're working on it.
He's our you're working
on Ms. SQL database
and there's a sequel Builder
which is provided
with those software use
the SQL Builder create
your SQL code copy the code
Connect using Tableau
to that database paste your code
here test your connection
and then boom you're done.
Then you will open up table.
You'll see feels like this.
They'll be coming directly
from your database.
And of course you will have
that option of connecting life
or creating an extract
like we do for Excel right
you can either make
a live connection
or you can create an extract.
So these options
will be available.
It's fairly simple.
It's not going
to be complicated.
They'll be one screen
after another choose
your database provide
your credentials choose
whether you want to live
or extract connection provide
your SQL query now,
we are blending data.
First thing you need
to know and of course,
I'm sure most of you
might be knowing it already.
There's a primary data source.
There is a secondary data
source primary data source
looks up in blue.
Secondary data source shows up
in Orange the first field
which you are going to pull up
into your Tableau environment
in your particular sheet
that is going to Define
your primary data source.
There's no set
primary data source.
Let's say we have sales
and promotion sales have 10
Fields promotions have 20 feet.
I click on this container
container for sales.
Okay.
Let me just see
if there is a dashboard.
I have pre-built
which has data blending feature.
So this is data
joining by the way,
you can see here.
This is data joining.
Let me just connect
to another data source
new data source Excel.
and I'm going to
connect to a Char B1
and I'm going to connect
to let's say performance rating.
Okay performance rating
and I will go back to the sheet.
So I've connected
to performance rating.
Now I have two containers.
Okay one and two
if I'm going to build a view
let's say I take last rating
into this field.
This becomes my
primary data source,
this becomes secondary.
Okay.
Now if I'm going to pull
it's a fixed salary
doesn't make sense.
I don't know
if it's going to work or not.
See these blue
and orange symbols.
I can do it twice over so
I can do it the other way
around I can pull something
from HR first this one
and then something from here.
Okay, and it's going
to go Almost similar.
This is primary
and the secondary so primary
and secondary is actually
defined by the order in which
you are pulling the data.
There is no fixed primary.
There is no fixed.
Secondary a default blend is
equivalent to a left outer join.
So, you know all
of you know,
what is left outer join.
We have two tables one
on the left one on the right.
It's like, you know,
imagine when diagram set theory.
Okay, all the components
all the data from left side
of being pulled and oh,
Only matching data
from right side is pull.
Let me just show
you left outer join.
Let's see if you hear this.
So left outer join everything
from the first table is pulled
and only the matching record
from the second table is put
this is default blending.
If you you know here
what I have done.
I have pulled something
from HR V 1 and then
I pull something
from performance rating.
This is left outer join anything
which is in HRV
one will be pulled
and only the matching records
from performance rating
will be put this is
a right outer join
everything from performance.
this bolt and only
matching record from HRV
one will put okay
so you can actually sort
of like simulate sort of,
you know left and right
joints by switching
what your primary
and secondary data source
if you filter out nulls,
okay, you can simulate
an inner join as well
but no full Outta joint
that's not available
through but blinding
how to create parameters
if we are using two or more
data sources one data source
have different data
and other have different data
how How can we use
a global filter
from two different data source,
if you are using blending,
okay filtering if
you apply filters,
it's going to work.
Okay, but you can also
use the parameter feature.
So what you need to do
is probably create
a calculated field put
that in the filter section
and it's going to work.
I'm not able to pick
up the proper example here.
Let's see.
We have employee ID.
And we have here we
have let's remove fixed salary.
Let's say we have
different departments.
Okay, you have to enable
the blending feature
on the correct field.
And in this was
just part of the tip
which I shared with you.
We have to still come
up to that level.
Okay, so while doing
and I'm just going to answer
your question on filtering.
Let me just complete this
and then I will show
you the example on filtering.
So sometimes you see null value
and this star symbol.
We just make sure
that you are clicking
on the right chain.
Okay.
So when you are doing
data blending you see this chain
like symbol and base.
On the field names so we have
last rating in our primary field
and in our secondary
field both and that's
where Tableau is suggesting.
You can create a blending
based on last reading
or you can create a blending
based on employee ID.
Okay, the correct field
should be employee ID
and you should enable that.
Okay, so just make keep
an eye open for that.
Otherwise, what will happen
is you will be able
to see this star symbol.
Okay.
It means that your data source
do not connect a contain
enough information to blend.
Okay, so just be keep
an open eye for that
now I have enabled.
A connection between employee ID
between performance rating
in HR now,
let me just pull up
number of employees.
Okay.
I'm going to pull up
number of employees
and I am going to apply
a filter on Department.
Let me apply
a filter on Department.
Okay, so you will not be able
to see quick filters.
You can just drag it here
and then you can choose
certain departments.
Let's say I choose development
and HR only say apply, okay.
Okay, and you can actually
apply this for 10 then
once you have dropped your feel
in this filter section,
then you can right-click
and choose show quick filter.
And then it's going
to work just well.
This is one way another way
is you can create
a parameter across
and the parameter
and a calculated field
in in your particular know
if both of your data source
contains the same values you
can make use of parameters
as well create a calculated
field use them in filter.
Like I have
done here Department.
Though it was
not a calculated field.
There was no parameter involved
but you can apply filters
in Blended data.
Okay, last rating
and department is last rating
if you want to show it as
quick filter you want to show
only high performance
may be in development.
It's going to work.
Well, no problem.
If you want to go into this
particular thing and you know,
you want to choose
some flow number
and show it as quick filter.
It's not going to be straight
forward you have to Drag it
first and then choose the values
which you want
to show say, okay,
and then you can show
it as quick filter.
And now I can apply
certain filters here and let's
say I choose all the Departments
so you can see those departments
which are working
out a fifth floor.
They are showing up.
It's perfectly fine.
You can do that.
You have to be a bit careful.
It blending is a bit complex.
I do agree and sort of like,
you know irritating also
from time to time,
but It works.
Okay, and you have
to keep in mind
that changing the blend
of the fields, you know,
the order of blending
or Blended Fields will change
the scope of your analysis.
Okay, this is something
which you need to keep in mind.
So let's say we have
this data set.
Let me just open up
a data set for you.
This is going to be a big one.
So I'm not going to open
up the main table,
but I'm going to open
up the lookup table.
sure, so we have Data
for different airlines.
Okay, which are flying in us
and then we have
a lookup table for airport.
So an airline will have
a departure airport
and Airline will
have arrival Airport.
In the main data,
I'm not going
to open up the data
because it's a big data and it's
going to take some time.
You will see in the main data.
You will see just
these codes iata codes,
but in your dashboard you
would like to show the airport
named probably okay,
and also which state the belongs
to which country they belong
to and these kind of information
you would like to fetch
in your dashboard.
So there will be
an arrival airport
and they'll be
a departure airport
if you blend your data.
Between departure
and this thing,
you know, I ate here you
will get description
of departure airport.
And if you blend
your data between arrival
and this particular key field,
this will actually show
up your arrival airport.
Okay.
So thing is the way you
are blending your data
will change the scope
of your analysis.
So you have to be a bit careful
for that as well.
There is arrival airport
does departure airport
and we have a single lookup list
for all different.
Airport if you blend
it from a rival,
it will show
you arriving airport.
If you blend it from departure
will show you departure a point.
We can blend more
than one data source,
of course, let me show
it practically to you.
So here it is
Airlines dashboard.
It's going to take a bit
of time to open up.
So I'm just going to continue
with the session
and then we'll come back to it
and I'll show you this example
where I've Blended
one data source more than once.
There's just one data source,
and I have brought it
twice in this dashboard.
Okay, so in the meantime Well,
it's opening up.
Let's proceed ahead
with charts are widely used on
on what particular situation
is best to use them so
bar graph line chart pie chart
scatter plot histogram box plot.
These are some
of the widely used charts
and when to use them anyone
here from my batch.
I'm sure they
will be able to answer
this question pretty well.
I'm not sure about the others
but do you have
some understanding
when to use a bar chart
versus when to use a pie chart
versus when to use line chart?
Let me give you
some context bar chart you use
when you have a categorical data
and numeric data may be
or if you want to show count
of categorical data.
So when you are trying
to compare categorical data,
Maybe male salary
versus female salary.
Number of male employees versus
number of female employees.
We have categorical data show.
If you are trying to compare
categorical data you use
bar graph line graph
is used to show Trend
over a period of time.
If you want to see
how things are moving
with respect to time is
your sales increasing is
your sales decreasing
with respect to time.
Mostly you use
line chart pie chart
is used to show the
percentage contribution mostly
in most of the cases you
can Use it exceptionally also
but most of the cases
pie chart is used
to show contribution percentage
contribution to hundred percent
of different components.
Okay.
So for example,
you have your employee base
what percentage of employer male
what percentage of employer
female don't use pie chart
if you have more than three
or four categories, okay,
it's better best
to use pie chart only
when you have to
at most three categories
if you have beyond that,
it's better to Use
bar chart then we
have scatter plot scatter plot
is used to show the relationship
between two continuous
numeric variable
when you want to
explore the relationship
between two continuous numeric
variable you use a scatter plot.
Okay.
So for example salary
of an employee versus age
of the employee.
Okay, you want to see
if older employees
are getting paid more
than younger employees
or maybe experience
of the employee
versus the salary is getting
These kind of relationship
if you want to investigate
you use a scatter plot.
Then histogram is used
to show the distribution of
a continuous numeric variable.
So for example,
you have weight
of different individuals
how how many people are
between way between 50 to 60
kg how many people weigh
between 60 to 70 kg?
Maybe if you are doing
analysis for an Airlines
and and you have some sample
data you would like to analyze
how much how many
passengers or how much?
Baggage weight.
Maybe you should provide
to your passengers histogram
will probably help you most
of the people carry
between 0 20 gauge is
to 30 kgs of weight with them.
It will help you analyze.
Maybe you can charge
some premium that histogram
is used to show the distribution
of continuous numeric variable
whether employees on
average they are earning
on higher side of their
earning all Louis egged.
Then we have boxplot box plot
again shows distribution
of continuous numeric variable,
but mostly used for exploration.
It will also highlight
the outliers for you.
Okay, so it will point out each
and every individual data point
on a particular box plot,
you will be actually
able to see what is
the distribution of a data.
It's not a very common graph
mostly used for exploration
because you know general public
they do not generally
understand boxplot you
have to take time and
For to explain them
what a box plot actually means
so mostly it is used
internally between analyst
for exploration purposes
how to be an expert
with Tableau formulas here.
I have provided a link.
This is like a reference guide
for you know online Tableau help
online reference guide you
can follow this.
Let me just open it up
and show it contains details
of different functions
which are available
within Tableau and with
example certain examples.
It's not very detailed.
As such you know,
each of the function
is not explained
through rigorous examples as
such but it's a good reference.
So we have numeric functions
string function date function.
Let's explore date functions
within date functions.
We have date diff data add
example here did this example
all these things are there
if you want a more,
you know tutor like approach
where you want to sort
of like get some context
and those kind of things.
Maybe there are some good books.
Written in Excel
for Excel formulas
which are very comprehensive
contains exhaustive information
about Excel formulas
many of these Excel formulas
are relevant to Tableau.
Okay.
There's one book.
I would like to recommend
formulas Excel 2013 formulas
by John walking bash.
We're pretty famous book and you
just need to read this part
but using functions
in your formulas tell you
text manipulation function date
and time function counting
some function select lookup
functions are Are available
in Tableau to some extent
not a great extent.
Okay, LOD calculations.
We are going to talk
about LOD calculations.
They are not going
to be described here in Excel
because this is a particular
feature of Tableau.
It's not a feature of Excel
and it's a pretty recent
feature LOD functions.
They it was introduced
in Version 9 only so
not a very famous
a such feature of tableau.
Next topic moving on.
What are the
different workarounds?
We use interval.
Actually, there are
many different workarounds
some we have already discussed
in performance optimization
and blending, you know,
like for example,
you can use one data source more
than once in a dashboard here.
Let's see.
Maybe you're here the data
is still being downloaded.
It's going to take some time.
So I'll come back to it
so you can actually blend
your data more than once
in a dashboard then I
talked about different.
It's optimization instead
of using IF else.
You can use Simple
calculation logic
like you know comparison sales
is less than 10
and then you can use Alias
to show it in a meaningful way.
So there's so many
different things available.
You can create charts
which are not available
by default in Tableau,
like hundred percent
stacked bar chart few people
are not aware of these tricks.
So I'm just telling
them to you here.
There is a hundred
percent stacked bar chart.
Let me explain hundred
percent stacked bar chart.
I'm not going to tell you
how it is created,
but you can create
this interview and you
might be knowing it already.
Maybe you do not realize it
or recognize it by its name,
but you might be you
might have seen
this chart already somewhere
maybe interview itself.
It's pretty easy to create
in Tableau a chart
which looks something like this.
This is hundred percent
stacked bar chart,
then we have donut charts.
Now.
This chart hundred percent
stacked bar chart is
not available by default.
Okay, so you have to take
certain steps to generate this.
We have donut charts again.
It's not available
in Tableau donut chart.
It looks something like this.
Okay, all these charts
can be created in Tableau.
And there are
workarounds available.
You can create infographics what
I infographics infographics.
Like these Jazzy Graphics
which are published in magazines
and all these are infographics.
All right, these
are infographics.
Maybe they'll be
some percentages and
some numeric values shown
infographics here might be here.
You can see 47 125.
These are infographics.
You can create them in Tableau.
If you want to you
can overlay other charts
over your geospatial map.
Have you ever created
a geospatial map
which has pie charts?
On top of it geospatial map
having pie charts on top of it.
Let's see if my data opened
up already here.
You can see just coming back
to the previous topic
as you can see.
I have Blended this airport look
up data twice my original data
it contains airport code.
Okay, and then I
have created a look up.
So what essentially I have done
is I have created a chart
geospatial map showcasing
departing flights number
of Flights departing for app
from a particular location
and number of flights arriving
at a particular location
geographical information
is available only
in this lookup table
not in your code data
as you can see I blended
the same data twice once
for arrival once
for departure, okay,
and you can also overlay other
charts over your geospatial map.
So for example,
you can create a pie chart
on your geospatial map.
You can do that just in case
if you are not aware already.
Let me show you an example
of that again.
These can be made available to
you through my class recording.
So just request
the support team to get access
to my class recording
and in case if you're
not already aware of it, okay,
and you would be able to see
how to create these kind
of geospatial map.
Anyone who's not aware
how to create these kind
of geospatial map.
You can see pie charts overlaid
on geographical Maps Okay,
so these kind of things
are possible so there.
This list can actually
be very long and there are
several different workarounds.
Just keep your eye
open keep your interest
in tact and Tableau
and you will learn a lot of work
around their wonderful thing
people have done one place
where I would suggest you
can explore these workaround
is Tableau public server.
So people publish their work
on Tableau public server,
and they have
done wonderful job.
I mean magical
things using Tableau.
So just try and expose some
of those examples
they have published.
T' try to recreate
them from your side.
Okay level of details
your what are LOD
when we will use them.
Okay, elodie's level
of detail expressions
are recently introduced
featuring Sky Blu 9.0.
And before they were introduced
there was this nagging
error message you will get
when you will try to mix
up an aggregate function
and an on aggregate function.
So for example,
you have different employs.
Different employees and you
have these employees
are earning something.
Okay, they are getting
some salary you want to see
how far away they are
from the average salary
of all those hundred employees.
So you want to calculate
average salary of
those hundred employees and you
want to calculate each employee
how far away he is
from that average salary?
Okay.
Now it's a combination
of row level function
and aggregate function
average salary is
aggregate function individual
salary is a role every
when you want to calculate
something like this salary
of an individual -
average salary of the entire
hundred people group.
If you were going to try this in
an earlier version of Tableau,
you will get this error
cannot mix Aggregate
and non aggregate arguments
with this function level
of details are a way
to address this problem.
Okay, and it's a big topic
and needs lot of discussion
due to lack of time.
We won't be able to take it up
so you can read more
about them here.
It's pretty
simple straightforward.
No rocket science.
There is this syntax,
which you need to be aware
of level of details
expression expression syntax
fixed include exclude.
Okay.
So using fixed you can talk
about I'm sorry using include
you can talk about dimensions
and exclude level
of details expression X
Plus, even you know,
to be honest enough.
I haven't used
this feature much.
Okay, because I have recently
migrated to Tableau
9.00 a couple of months back
and never really required
to use this function,
but Seems fairly simple
and straightforward
and there are certain examples
given in fact for simple
straightforward calculations.
You can aggregate
the function like this,
I think so.
Yeah, here it is.
So this is not going to work
but this one is going to okay.
So this has level
of details expression
embedded So within curly braces
that we could just go
through this article.
I've provided the link in here
and you'll be able to See
more details on level of details
of this particular error message
was pretty nagging.
Let me tell you A lot of times.
I've tried to do things
which didn't work out
because of this particular
limitation of Tableau
and level of detail seems to be
a pretty exciting feature to me
and I'm pretty sure
you're going to encounter
it frequently extracts.
What are extracts extracts
will increase the performance
of your dashboard.
They it's a way
in which table you
will extract the data keep it.
Side optimize it
for faster performance.
There are certain formulas
additional formulas
which becomes available
when you create an extract.
So for example count D formula,
there is a typo here.
It should be count.
Di not counted formula
should be County formula
and I'm talking about earlier
versions of Tableau.
I'm not sure
if it's available by
default interview 9.0 and above
but County formula was
not available by default
unless and until you
are creating an extract
this distinct count for me.
Le was not available
to you only for extracted data
and you can control Refresh
on ever-changing data source,
if you are using extract, okay,
if you if you make
a live connection data changes
your Tableau report changes.
Okay, if you have scheduled
refresh in extract,
you can control the refresh
allow users to refresh the data
based on their preference
or you can schedule it also
if you want to so
this lot of control
which you get on data refresh
by using extract Good feature,
especially improves
the performance how to
refresh your visualization
from your desktop environment.
So go to data,
okay here let's say if you have
this data source go to extract
and click on refresh.
If you have created an extract
if you have live data,
you can refresh it
right from here.
What is data visualization?
Okay.
Now we are coming
to common interview problems.
These problems have been
asked by you question.
Number two question number 12
have been asked by The Learners.
Okay.
Now some common
interview questions.
What is data visualization
data visualization refers
to the technique
used to organize
and present information
intuitively using
different visual techniques,
and it enables you to quickly
answer this question
and your data becomes
a competitive Advantage instead
of an underutilized asset.
If you're going to show
your data in tabular format,
it will be very
concerned, you know.
Confusing for the users
to digest that information
if you show it visually
in form of charts
and graphs it becomes visually
appealing more user-friendly.
So there are different
techniques of data visualization
which involves things
like when to use
a particular kind of chart
and what kind of situation
what kind of color themes
to follow some of those things.
We already discussed in some
of the previous questions.
Okay.
So all these
encompasses the field
of data visualization making
information relevant connected
to each other
enabling quick slicing
and dicing of data filtering
of data pivoting of data giving
users an option to pass on
feed inputs through parameters.
All these are part of
data visualization techniques.
What is the difference between
quick filter normal filter
and context filter
quick filter is like
when you right-click
on your visualization,
you know, you create
a quick filter
like I've done here.
These are quick filters.
These are normal filters.
For example Department name
in your if you want
to edit this filter,
you can go and choose wildcard
condition some condition top
and numbers all
these things you can do.
This is normal filter.
These are quick filters.
So this is again a common
question people may ask you
what is normal filter
was a what is quilt
and then we have context
filter context filter
is when so what happens
in Tableau is when you
are applying normal filter
or quick quick filter,
they are going to be
independent of each other.
Let's say you have again HR data
set you are going to filter.
Data based on gender.
Okay.
So you have applied a filter
on female employees.
Then you want to filter data
based on geographical location.
So let's say you have applied
a filter on India.
So what is Tableau going to do
table is going to apply filter
on female employees.
And then again,
it's going to apply filter
on all the data set, you know,
the end all the rules on India
and then it will show
you that data.
There are some drawbacks.
Backs of it.
So what happens is let
me show you an example
on this HR data set itself.
So I am going to pull
up Department name
and six salary and I will show
let's say I have a salary
and I'm going to take gender.
supplier filter of female here
And Department name top,
it's empty king top three values
by fixed salary average salary
as you apply, okay?
Let me just remove the gender.
I'm really not able to think
of a proper example in here.
So what actually happens is
when you apply normal filter.
They start from scratch
for each filter you applying.
Okay?
Let me see if I can simulate
that thing in you.
Let's see.
We have chosen
both male and female.
So top three departments
eeo office HR and testing okay
if we apply a filter of female.
Okay, then again,
you can see see you
office HR finance
and facility and testing.
Okay.
What if we take this gender
and keep it here?
We have to put a context a
actually it's not going
to be visible
until we put a context
where the so
what context filter does is it
will apply a filter by default.
Okay, so you can restrict
Department name here.
And then if you pull in gender,
let's see the results
actually changes.
It's not actually changing
in this particular case.
I've chosen a lame example.
Okay, so context filter
what it does is it will apply
a filter at first level
and then rest of the filters
which you are applying
will be applied later on.
Okay.
So the next set of filters
will actually work
on filtered data.
So when you apply
a context filter a new data
set a temporary table
will be created
and then your rest
of the filters will work
on that temporary table.
Okay.
Going to speed it up your thing
speeding up your dashboard.
And also when you are using
certain situation like top
10 in my particular example,
it didn't work
because that was
not really relevant.
I'll pick up some data example
and probably post it as
a article on the Tableau
this blog section.
Okay of a Eureka
and there you can see mostly
for top and results.
We use this context filter.
So we have normal filter.
We have quick filter.
And we have context
filter context filter
will create a temporary table
and then other filters
which are going to apply
will get applied
on the context filter.
So it will be level filter
one filter applied.
Second filter will get applied
to the filter data not all
the data usually used for top
n values extracting top
n values these kind of things.
Okay, what is data blending?
We have already discussed it.
So I'm not going to go back
to this discussion again.
And when do we use data?
Learning so data
blending is used
when we are pulling in data from
different different sources.
For example, we have
some Excel worksheet.
We have some table
from Oracle database.
We have some table
from Tableau server.
So in that case we can combine
them all into a single View
using data blending
but given a choice.
You should always go
for data joining
if your data is coming
from the same data source,
let's say you have
one Excel file
which has multiple tabs
and and you have an option
of either blending the data
or joining the data go
for data joining.
If you have an option,
it makes things more
intuitive straightforward
and all data blending
is generally used
when you are working
with different data sources
another important question.
This one is quite
important actually.
So what are the differences
between TW B LT W BX?
So twb is General Tableau file
and TW biessed is
Tableau packaged workbook.
So twb Tableau workbook and TWP.
This Tableau packaged
workbook W packaged workbook is
like a complete set
of data plus any image,
which you have kept
in your dashboard.
All of them combined
together into one.
Zip file T. WB W workbook is
just a set of instructions
which table utilizes
to draw your visualization.
Okay, it doesn't
contain any data
if you have used a picture
if you have been
embedded a picture
that is not going
to be available.
In fact, you can open up
a twb document by right-clicking
and opening it up in a you know,
if you have notepad
plus plus you can open
up the twb and see
the code behind it.
It just just contains the
instruction there is no data.
So how table you
should repaint your date
on screen those instruction.
That's it.
What are the difference between
groups and set difference
between group and set
can be a bit confusing
for some people actually,
you know, you can improvise
their functionality
and Make them behave
similar to each other.
So but essentially
groups is something
when you club members
individual members together
into a group set
on the other hand
will create an in-and-out sort
of a feature for you.
So for example,
we have job title.
I can go and create
a group here.
I can say co-manager
senior manager SVP and VP.
These people are People manager.
Okay, and these people
are the people associate
Junior associate senior
associate SME support staff.
These are individual performers.
I can do that.
Okay, I can say okay.
This is how we create
a group then we have set.
Okay instead what we
do is we create a set
so we'll say create
and then we create a set.
Okay, and then we
can apply a condition.
Let's say You want to create
a set of all those people
whose average fixed salary
is may be greater
than let's say 10.
Okay, and you you can say
this is a high learning people.
So this is how you create a set.
Okay, and then you
once you create a set then
what happens is you can pull
the set into your analysis
and maybe you know pull in
some other details maybe
and then you can see
this in and out.
This is one way or you
can use it as a filter
so you can just right click here
and you can choose
this option show member inside.
So what will happen only
those people where those cases
where the learning is high
will show up so here
for each of them.
The salary is going
to be greater than 10.
Okay, as you can see
so it can act as
a filter on the fly.
If you choose
this option Show members at
if you choose this option,
you'll see how many people
are lying in that range.
How many people are
like be on that train?
So basically they
serve different purposes,
but you can improvise them use
them along with formula
and make them behave
like each other.
So for example,
I can create a group
and then I can create a formula
on this offense formula sort of
and then I can use it
simultaneously to create
like a set like feature also,
you can create unions
and intersection inserts.
I hope you are aware
of all those things
so you can create a union
of sets you can create.
The intersection of set all
these things you can do.
So these are
differences page shelf.
What is spatial you
can analyze data
on Tableau using a feature
called page shelf by
shelf will actually
create different pages
of your different situation.
So for example,
if you have I'll show this
actually I'll show page shelf
in action to you.
You can create animation
like feature using page shells.
You know, you can see
different department
and their head count Trend
over past five years.
So pmo versus testing was a CEO
of is versus development
versus HR all these different
things are appearing.
So what actually happened
was I put this information
in page section.
Let me just open up
this sheet for you.
I put this department name
in the page section
and then Tableau
created separate pages
in telling together
for each of the department
and then you can play
them together and create
this animation like feature.
These are also called
as motion charts
in case you may hear different
versions of this question.
What is spatial
what our motion charts
and how you can create animation
like features interview.
So for all of them answer
Remains the Same and here
is a detailed usage guide
for page shelf explain.
When would you use
join versus blending
in Tableau again?
You have the same question.
I've already told this
to you blend is used
when you are getting data
from different data sources.
Okay, and joining is
when you are getting data
from same single source.
So for example all data coming
from same Excel file all data
coming from CSV files
you use joins one data
is coming from CSV.
Another data is
coming from Excel.
Another data is coming
from Oracle you use blending?
Okay, if given a choice always
go for join it makes more sense.
Or intuitive everything
in one single place.
No need to enable the link
anything like that.
No need to Define
primary calculations.
I can recalculate these things
are not required.
When you use a joint does
that make sense?
We have talked
elaborately about joins
and blending multiple times.
We have I treated this.
It's an important question.
That's why okay,
what is default
data blending join?
I've already talked
about this data blending is
the ability to bring data
from multiple data sources
into one Tableau View.
That is what
data blending is in,
you know people
from database background.
They tend to think
of data blending as
like join data blending
is not actually a join.
Okay Tableau picks up data based
on primary data source,
it looks at primary data source,
then it looks at secondary data
source and tries to match them
and pixel pick up data that way
but it's not actually a join
which is going on
behind the scene.
So there is A bit
if you try to think
of it logically
or you know data blending
is not actually join.
No additional column is created.
Okay.
So when you do a join
new columns are created
your data changes sort
of okay in blending.
Your data doesn't change.
It's just a method
of bringing fields
from different data sources
together in one single view.
That is what data joining is.
I don't know
how it works behind.
The scene but how data blending
actually you can utilize
in different Innovative things
different innovative ways.
Okay, so sales,
you know that you can actually
capture date of the sales
and you can compare
the monthly headcount
from a different data source,
and you can blend
them one section.
You can aggregate
the date into month.
Another section
already has month.
You can blend it
one calculated field one course.
Field okay, you can
blend them together.
No problem at all
blending you can do
in multiple different ways.
You're just limited by
your creativity at times
it can be nagging I do agree.
So at times you
may encounter problems,
which doesn't make
logical sense,
but then it's just the way you
have defined your blending.
You can if you change the scope
you can make it work as well.
I've done pretty crazy branding
and I've seen people doing
crazy kind of blending
and creating wonderful
results blending is
a powerful powerful feature,
but given a choice go
For data joining it's easier
and intuitive also
and you can emulate left join
right join inner join
by setting your primary
and secondary data source
the way I've explained
to you already.
So if you choose
your primary data source
and secondary data source
will be a left join.
If you flip them.
It will be a right join isn't
what you're choosing as
your primary and secondary
if you filter out null values,
it will become an inner join.
Okay, there is no option.
Of creating a full outer join.
What do you understand
by Blended access
in Tableau measures
can share a single axis
so that all Max are shown
in a single pane
instead of adding rows
and column to The View
when you blend measure there
is a single row or column.
Let's see this one.
I think we are talking
about dual axis in here.
Oh blending of Access compare.
Okay, so essentially,
you know when you have
two different measures,
Can actually blend their axis.
It's like let me see
if I can create an example
for you we have.
Number of records
and we have date of joining.
Let's say and we have created.
a trend line and let's see
we have different
department names in here.
So let's see.
I mean essentially
if you blend the access
everything will come
in one single way.
Actually it stands for.
When you have two measures
not two Dimensions,
but two different measures
so we have this
and we have this fixed salary.
So I'll take fixed salary
and put it in here.
You can then you
can blend the axis.
Are they talking
about dual access
in here might be I think
that's what they are doing.
This is blending
of accesses what they
are saying showing okay
and then you can sync them
if you want to so we
have two accesses here
if you want you can sync them.
So Let me see
if I can sing them together.
Okay, I'll get back to it.
I haven't used will
access for a while.
Now.
There is an option here
through which you can sing
the axis actually, okay,
and that will create
the same access levels
in both the fields.
So essentially one line will be
on top another line will Crawl
Through the bottom.
So yeah right side.
We do have this option.
I'm not able to see it.
You're maybe going to because of
scope of my calculation
or some some problem
synchronized dual axis
option is there.
Where do I don't know?
Oh, I have to enable dual axis.
First.
Is that dual axis enabled?
And okay, so I have to remove
include 0 sorry from here.
And then right now
this option is not enabled
for some reason uniform access
may be independent fixed.
It's not working right
now synchronizing synchronized
will access is not working
for some reason maybe because of
scope of my calculation.
I'll check this.
Okay why this is happening.
I apologize for this one.
This option is not actually
very difficult to use
for some reason.
It's not working right now.
Okay, what is story in Tableau?
This is again a new feature.
I think it was introduced
in version 8
if I'm not wrong and story is
a way to present your findings
or your analysis
in a step-by-step manner.
That's it.
I mean Story Probably
is a fancy name.
It's nothing new.
So basically you
create your worksheet
and your dashboard story is
a way to put them in sequence
so you can give
your users a guide.
That tour.
Okay, like if you trying
to present something
some findings interesting
findings to you user.
You can first build the context.
Let's say we're talking
about HR dashboard.
Okay chart Workforce analysis.
We have done of a company first
point you would like to say
this is the workforce profile
of our company B's
many male employees.
Are there these many
female employees are there.
These are different
employees working
in these different grades.
So you will create
your first story line,
which is This is our HR profile
Second Story point you
will compare the ratio of male
and female and you will say
there seems to be a gender bias.
If there is a gender bias.
Okay, if you are investigating
on gender bias third story point
you can see where
this gender bias is coming from.
Okay, so you will say okay.
These are different departments
where the gender bias
is the highest.
Okay, then third
fourth story Point what
what might be the reason you
can provide some hyperlinks
some external sources.
For the reason then
fifth Story point maybe you
can show the revenue stream
of those Department
which have healthy gender mix
and probably they're better.
Okay absenteeism rate
is probably less
in those department
where we have healthy gender mix
and then you can put
your final point
that you know,
we should have
healthy gender mix
so story is nothing
but a sequence of worksheet
and dashboard to convey
information in a more
meaningful manner.
Okay.
It just gives your uses
a guided tour.
They don't have
to hop for information.
One place to another without
knowing anything you guide them.
It's like those slide shows
which are available on Yahoo.
And you know different websites
where you click on that arrow
and it takes you
to the next picture
maybe some Bollywood story
or some you know,
some fancy story.
They want to share with you.
Similarly.
You can create a story
in job you as well make sense.
So these are story points.
And what is the difference
between discrete and
continuous interview?
It's a actually
consistent difference.
It's like part
of all round analysis not just
in terms of Tableau.
What is discrete and what is
continuous discrete is something
which has limited number
of values discrete values
are counted as
distinct and separate
and can take individual values
within a range.
So for example number of Trades
in a sheet customer name
or row ID or state?
Okay gender for example coming
to my HR example
gender or departments.
These are discrete values.
Okay, if if you are talking
about numeric values, I mean,
these are actually categorical
values to be specific discrete
and continuous is more
when it comes to numeric values.
Okay.
So discreet is something
which has limited.
Were of values for example
your performance rating.
Let's say your performance
rating is given in value
of 1 2 3 4 5 where one is
it's a the highest rating
and five is the lowest rating.
This becomes discrete data.
Okay A person can get a rating
of one or two or three or four
or five continuous data
on the other hand
takes continuous value
like it can contain any value.
It can have any values
for example the age
of a person Can be
25.3 two years,
you know height of a person
can be 5 feet 6 inches.
Okay, it can take any value.
So continuous versus discrete.
These are common terms
in analysis general terms
and then we have
categorical values as well.
Go categorical versus
numeric values measures
and dimensions for
these kind of things.
So discreet values
which can take stepwise
values continuous values,
which can take any values.
How do I automate report using
Tableau Software to automate
the report do your work
and create your dashboard
publish it on to W server.
You will have the option
of scheduling the report
you schedule the report
when you want
to get it refreshed
when the users will
open up the report.
So Tableau server
will automatically run the job
for you based on the time
which you have provided to them.
So let's say month start
first date of the month.
So 12 o'clock,
whenever the date changes
its going to fetch the data
refresh your visualization save
it users will come
next day open up.
The report will be able
to see new information
every month start.
They'll be able to see that.
This is how you can automate it.
How can we combine database
and flat date file data
in Tableau desktop,
of course through data blending.
So you pull data from your table
and flat file and then go
to data edit relationship
if you want to so,
for example here,
I have done some data blending.
I've got two data sources here.
If you remember two data sources
this in this I go
to data edit relationship
one data source
to Second data source,
you can define
a custom relationship.
Also if you want to
or let it be Custom I
do not want my blending
to be done on rating.
I just want it to be done
on employee ID
and then say okay.
I'm sorry this sheet 13.
Nita and it relationship
this is what you want.
So this relationship
has been defined now
and then now you can pull in
data from these two
different data sources
and you will be able
to blend them successfully.
So these steps you need to take
if you have two data sources,
they have common column names
you do not need to do
it this data and edit
relationship is not required
if they have common
column names and if that's
what you want to blend the data
on then you do not need.
Go to data and relationship
that's not required.
But if the name is different,
then explicitly you have
to Define this so just
be careful about this one
and then next step
you go pull data
from one data source,
go to your second data source,
you'll be able to see this chain
like link enable it
and then perform your analysis
is drag the fields
which you want to then go back
and you can take
employee count whatever,
you know, so you
can perform you on this
is this is how blending work?
X what are the platforms
Tableau server can run
on Tableau server can run
on Windows and Mac.
I don't know
if this might be
a very relevant question
for a tableau developer.
But yeah, okay.
How do you publish table reports
to Tableau server
to publish a report go
to server choose this public
publish workbook option.
It will ask you
for your credentials
and you have to provide
your credentials.
You need to have access
to a tableau server.
Okay.
So first you need to probably
click on the sign in.
Provide your credentials
then choose publish workbook.
It's very simple go
to server sign-in provide.
Your credential published
workbook will give you the name
of the workbook,
which you have saved by
you can change this name.
If you want to you can provide,
you know user
permission as well.
Let's say you have
10 different sheets
and you want to publish
only few of them.
Just check them.
Whichever you want to publish
and check the one you
do not want to publish
it will still be part
of your Tableau desktop file,
but it won't be published
to the So, okay,
and then you say publish
and it's going to work
you can include external file
show selection these options
you can enable it's
very simple all menu-driven very
easy thing Define
parameters in Tableau,
and they're working
Tableau parameters are defined
variables values what
our parameters parameters are
away to accept inputs
from your user.
Okay and let them interact
with your dashboard.
Okay, and you can place
parameter controls.
Want your dashboard users
can pass on values
through those parameters you can
use those values in calculation
and filters here is an example.
You can create a calculated
field value returning true
when the score is greater
than 80 and otherwise false
using parameter one can replace
the constant value of 80
and control it.
Dynamically dynamically
in the formula.
Okay, so it's parameters are
a way to take user control.
Okay.
So these are parameters.
What are the difference
between Tableau desktop?
Tableau server Tableau desktop
helps you create data
visualization workbook creation.
These kind of things it
will help you do Tableau server
is used to distribute
these workbook to the end-user.
These workbook
can be interactive.
You can enable your users
to apply filters slice
and dice the data pass value
through parameters,
and all of these functionality
will be available
through Tableau server.
It will be available
just in case
if you do not have access
to Tableau server.
Does Tableau desktop also
become redundant to you?
No, not at all.
There is Tableau reader
which you can still use
for offline viewing of the file.
So you can ask your end users
to install Tableau reader.
It doesn't involve any cost.
You can create your work
using Tableau desktop,
pass it on and they
can use Tableau reader
like a PDF reader.
Okay, so we have acrobat files
these Adobe Acrobat files.
There are software
to create those files
and there are software's
which are supposed to just
read those files tab you.
Dinner is such a software.
It can read your dashboard.
All the interactive features
will still be intact.
It's just that the user won't be
able to create anything.
No new charts.
They won't be able to create
a formula or something,
but they can apply
the filters quick filters.
They can use they
can click on chart
and you know slice and dice
the data all those things
will still work anyone here
who wants to learn about
hundred percent stacked bar.
Let me just quickly
show it to you.
It's pretty simple
so stacked bar.
Let me Quickly show you
how to create a hundred
percent stacked bar.
I'm going to show you
male versus female ratio
the thing which we
talked about number
of Records this data.
This is HR data set we have
different departments employees
working on those departments
and we have male and female
employees gender demarcation.
I've created a quick pivot table
like summary table.
So we have CEO
of is and development
HR Finance male-female all
these things we have, okay.
Let's create it
step step by step
and I'm going to show you
how a stacked bar plot
is created a hundred
percent standby power
so different departments
and number of employees working
in those Department.
I'm going to create a bar plot.
First of all.
And then I'm going to flip it.
So, you know,
it's up to you
if you want to flip it or not.
So CEO of his development
HR Finance BMO testing
and then I'm going to take
gender through it in color.
So we have male and female
employees working in each
of these departments.
I want these bars
to be of equal height
and I want to show
values as percentages.
Okay, so I'm going
to take number of Records.
I'm going to throw it in here.
These are male employees
working in development.
These are female
employees working in HR
and I'm going to click here
quick table calculation show
it as percentage of total,
but then what has happened
this is actually percentage
of entire total.
I want this hundred
this particular bar
to be a hundred percent.
So these two should add up
to a hundred percent
these two should add up
to a hundred percent right now.
It's not happening.
Why because the scope
of calculation is
like this where everything
if you add up Get the
will become hundred percent.
I want to edit
that table calculation.
So I clicked on this drop-down.
Choose edit table calculation
table across I'm going
to choose table down instead
of table across a apply.
Okay.
Now each of the bar is adding up
to a hundred percent,
which is what I wanted.
Okay.
Now these different bars broken
up so each bar is now adding up
to a hundred percent,
but I do not want to showcase
that Co offices like,
you know tiny
and this development is biggest.
I want bass to be
of the same size.
Because what can I do?
I can click here now.
And this is a work around
this is not straightforward
and intuitive and
that's why I kept it
in the workaround section.
I'm going to go to this section.
What is this section defining?
It is defining this axis.
That's why I have to go you
I have to set the axis now.
I set the values I
have to set the axis.
Quick table calculation.
I'm going to say it as
percentage of total.
Okay, and then again Same step.
I'm going to perform table
across table down.
I'm going to choose say apply.
Okay, this is
my hundred percent stacked
bar chart showing clearly
that CEO of is has probably bad
whatever gender ratio.
This one is good HR finance
and facility this I was not able
to interpret properly here.
Why because this bar was showing
up as like small and all but
if I convert this
into a hundred percent
Stark bar I can see
this portion is biggest for HR.
This has good gender ratio this
how you create a hundred percent
start barcode will move on
and we'll take a look at
the success stories of Tableau.
So almost every company uses
tablets that business
intelligence tool and all
of this company
that you can see in front
of you in the screen.
They all use Tableau
starting from US Air Force
to Burger King's Citibank,
you can see these are all
all different companies,
but they all use tablet.
These are a few
of the comments made
by the most influential people
from a particular company.
You can see Ryan Greener
from Deloitte says
that Tableau is changing
the game for us.
It's reduced the time
that they have to spend
on Lower value add activities
similarly Gerard and namesake
from one Kings Lane say is
that it increases our sales
it decreases our cost.
There is a direct impact
it just gets you in.
I'd Foster and you can read
for the other two as well.
So there are
many success stories.
People are absolutely
loving Tableau and
once you use it,
I hope that you love it too
because you can do
and you can play around
with data in different kind
of ways with Tableau.
Thank you for
watching this tutorial
and I'll see you next time
till then Happy learning.
I hope you have enjoyed
listening to this video.
Please be kind enough to like it
and you can comment any
of your doubts and queries
and we will reply them
at the earliest do look out
for more videos in our playlist
And subscribe to Edureka
channel to learn more.
Happy learning.
