hey guys welcome to the session by
Intellipaat the world of IT is driven by
data as we know it and the workforce
behind it is full of data scientists and
data analysts majority of the IT
companies use data science and data
analytics to make data a friendlier
entity and drive businesses to success
and in this session we'll be looking at
various aspects of data science and data
analytics before moving on with this
session please subscribe to our channel
so that you don't miss our upcoming
videos now let us take a quick glance at
the agenda we will start off with a
quick introduction to the world of data
and learn the basics then we'll do a
good comparison of data science and data
analytics moving on we'll look at the
different skill sets of a data scientist
and a data analyst finally we'll look at
some sample job descriptions and their
corresponding salaries also guys if you
want to do an end-to-end data science
certification intellipaat provides a
data science architect master's course
which you can take up and master all the
Data science concepts and the link is
available in the description now without
any further delays let us begin with
this session so let's begin the session
by checking out the introduction to the
world of data well as we already know it
big data has become one of the biggest
component in the world of technology
that surrounds us today and thanks to
all the analytics that Big Data provides
us we can pretty much gain insights from
the data and this can results in
businesses which can glean which can you
know you know be superior which can
understand how the data works and then
use it to its advantage well if you
already know the amount of data that
surrounds us we have petabytes and
petabytes of data being put out by these
companies and even they would require
the same Twitter Facebook Instagram
YouTube when you think about it so these
are the companies who will use a lot of
data store all these data ends and go
about making use of it as well so this
should bring us to the question of what
data science is well later science is a
huge field which is focused on basically
finding some actionable insights from a
large set of raw data large sets of
unstructured data and structured data as
well you might be wondering what the
goal for data scientists is well a data
scientist main goal is to basically ask
questions you know locate wherever he
can make use of the data and know where
the data can be put forth for answer
specific questions using the data add
emphasis and find the right questions
that he needs to ask towards the data as
well guys so data scientists are
basically concerned with all the data
that moves into their hand from the data
engineers who engineer all the data and
bring in the data to the form and these
data scientists pretty much go on
analyzing the data most of the time it's
performing some sort of calculations
performing analysis and eventually it
involves applying machine learning
artificial intelligence or any of these
very big concepts to make sense of the
data drive future trends and whatnot and
so much more and later this is passed on
to the data analytics team as well guys
so on that note what do you think about
data science head to the comment section
and let us know and this brings us to
what data analytics actually is well as
I've mentioned data engineers drive the
data into the company data scientists
work with the data to get it to a stage
where data scientists take the raw data
and convert it into information and this
information is used by data analysts who
pretty much go on processing these data
and performing a lot of statistical
analysis on all the existing data sets
which is provided by the data scientist
or they may pick up something new and
perform some analysis on the same as
well but then analysts also concentrate
on creating methods where our data can
be visualized because at the end of the
day the data is just numbers right so
seeing just numbers might not make sense
at all times so converting these numbers
into very good looking visuals very good
looking graphs and driving meaningful
insights from there to drive the
business basically to success is the job
of a data analyst so this data analyst
will find out all the current problems
and establishes the best way that they
deem fit to solve the problem so the
field of data and analytics is pretty
much involved in you know solving all of
these problems for all the questions
that we do not know the answers to
basically so when you come in to think
of this point of time most
the questions we asked us because we do
not know the answer to but when you're
talking about data you can ask and
answer tons and tons of questions but at
the same time you need to use those
answers pretty much to the same pace and
the same trend of the business
eventually to drive something meaningful
from it right so again this pretty much
will lead to results which will lead to
improvements as well so this brings us
to the main highlight of this video what
is the difference between data science
and data analytics well the first main
thing that you need to know is that you
know while many people actually or you
know change their on these terms data
science and data analytics and use it
together there is a very keen difference
because the main difference in my
opinion is the scope in which data
science saw you know spreads its wings
in and data analytics is good and so we
can consider data analytics to be a part
of data science guys so data science is
the umbrella and the analytics analytics
is a part of that so coming to the
differences well data science is a
complete umbrella term for a group of
fields again which is basically used to
mine large data sets which basically
means that we'll be you know looking at
hunting into large data sets and driving
meaningful insights from them but at the
same time data analytics is a more
focused version which is basically you
know considered to be just a larger part
of the entire process because the end of
it all the data collected by the data
scientists can be used by the data
analytics team to pretty much go about
doing visualizations analyzing trends
and so much more so coming to the next
point it's pretty much think bigger
picture think data science because data
science isn't concerned with answering
any specific queries with respect to a
specific set of data it works its scope
in a very wide way but when you talk
about data analytics data analytics
works extremely well when you have a
small scope to work with you know when
it is focused and you know the questions
which need the answer to and these
questions the answers that you can
derive from these questions are
basically from the existing data which
is already present so this is another
simple difference between data science
and data analytics coming to the next
one
well data analytics is all about the
present data and the future well let me
tell you again data science pretty much
you know produce this broader
and you know that you should concentrate
on the questions which should be asked
based on the past data where you can
mine the data and make sense of it this
is the data Sciences team but then
coming to data analytics here we
basically you know emphasize we
basically drive into you know
discovering answers to questions that
are being asked at that very moment at
that very instant to drive something in
the present or to produce a trend or an
analytics for the future case
so again when we've been talking about
scope here's a quick reminder slide for
you guys data science has a vast scope
it has a microscope while data analytics
is more preferred for a microscope so
what this basically means is that data
science is a more vast field and as
we've already spoken about how data
analytics come under data science so
your data analytics is used to do
certain set of tasks really well and a
data scientist can also go about doing
all of these tasks as well but then
there are many other tasks are for data
scientist that a data analyst cannot do
and this brings us to the goal when the
goal of the data science field is
basically to ask the right questions and
get the right answers from the data ok
guys a quick info if you want to do an
end-to-end data science certificationIntellipaat provides a data science
architect master's course and those
details are available in the description
now let us continue with this session
but then the data analytics goal is to
pretty much find some actionable data
where you can make sense out of it and
are you know drive business decisions
and more and this brings us to all the
major fields are that we can go about
using you might have heard of the term
data science in majority of the fields
in today's world when it is used in
machine learning it is used to achieve
artificial intelligence you know search
engine engineering corporate analytics
it is used in the field of for medicine
as well it is used in the field of
manufacturing and so much more data
science has its foot deep in today's
world of technology and coming to data
analytics data analytics has been a boon
to the field of healthcare to the field
of gaming travel a lot of other
industries again manufacturing
industries to where the data requirement
is immediate and the analysis and the
analytics which goes with it is also
immediate guys so this brings us to the
question whether data
and data analytics are the best of both
worlds well think about it this way the
two fields basically you know can be
considered as two sides of the same coin
because again when you think about it
it's one coin where their functions are
extremely highly interconnected but then
there are two phases to the coin where
they are actually a bit different from
each other and this brings us quickly to
check out all the skills of a data
scientist and a data analyst case so
basically a data scientist as we have
discussed pretty much works with all the
data he or she gets and applies a lot of
mathematics on it applies a lot of
statistics on it and so much more to do
that the primary skill that a data
scientist requires is pretty much having
a strong knowledge of languages such as
Python R SAS scala and so much more
then the second thing is pretty much
knowing how the person can work with
unstructured data because at the end of
the day a data scientist can be working
with images can be working with videos
can be working with music or text data
with unstructured and so much more and
then this brings us to some back into
development where the person at the data
scientist needs to have some knowledge
over back-end development with respect
to databases data warehousing handling
data on the backend and making the
backend talk to the front end as well
guys and this brings us to the fourth
skill which is basically having the
knowledge of machine learning and
sometimes deep learning as well because
at the end of the day we can use machine
learning and deep learning and all of
these concepts to achieve artificial
intelligence and make the data a little
smarter this brings us to the skills
that make a data analyst when a data
analyst basically works with the data he
or she gets and at the end of the day
creates beautiful visuals out of it
gives us future trends predictions and
so much more having the knowledge of
statistics is one of the most vital
skill for a data analyst but then having
very good statistical skills without
implementing it in the world of computer
science would not make sense hence a
data analyst would also be benefited if
he or she knew a bit of programming in
terms of Python or language such as R as
well then coming to data wrangling again
you've talked about data and we know how
data can be so much messy especially if
it's unstructured or data can be a very
unruly entity if you may so data
wrangling is again basically you know
are picking up all the data that you
particularly require in a hunt it's like
hunting for a needle in a haystack and
data wrangling is pretty much something
similar to that as well and a good data
analyst will have very good data
wrangling skills guys and then at the
end of it a little bit of Big Data
technologies are needed as well you need
to have knowledge of big and
technologies like high and more so to
bring it all in one single gist here is
on the screen so basically a data
scientist is responsible for working all
of the raw data and converting it into a
Valid information a data analyst would be
basically performing prediction and
presenting all of these informations to
clients and peers well think about the
scenario where a data scientist gives
lots of numbers to data analysts and
then a data analyst is a person who can
figure it out he can convert the numbers
into graphs but then when you're going
into a business meeting or you're going
into say you know a Board of Directors
meeting giving them the numbers they
might not be able to make more sense of
it so basically you convert these
numbers and make them look into
beautiful visuals very good graphs pie
charts and whatnot and all of these
eventually can make sense because at the
end of the day a part of the data
analysts job is to basically convince a
person who doesn't know the technology
eventually about the data that they have
been working on so this brings us to a
couple of sample job descriptions that I
want to show you guys with respect to
data scientists and data analytics as
well well the first job description I
have is a for data scientist job
description and dog be can the company
who's hiring is the New York Times they
require a PhD ms or 3 plus your
experience in computer science applied
mathematics or quantitative and
computational discipline as well again
as we have talked about mathematics is a
very important part of our data
scientist mathematics is a very
important part of a data scientist job
role and then they'll require more than
two years of experience with open source
machine learning concepts statistical
tools experimental designs and so much
more again the one of the most important
skills they require is coding so python
is preferred or even arc to us well
but then the New York Times says they
prefer Python developers and then again
ability to communicate all of these
complex ideas in data science to
relevant stakeholders and making them
understand of what's going on and lastly
it's pretty much you know data
engineering experience where you can
handle the data coming into the form and
then go on working with it you will need
to work with the data and the backend
using structured query language and
you'll have to manipulate large
structured and unstructured data which
is sent for for
analysis well guess so this brings us to
an data analysts job description we have
the job description from uber uber is
the very famous cab providing service
across the world when they require a
Bachelor of Arts science or masters in
economic business in economics business
engineering operations research or any
other quantitative focus and they
require excellent analytical thinking
where you have the ability to see
through the data connect the dots
between the data and pretty much you
know gut check every number you see
well gut checking pretty much means that
you can go about analyzing these data
and know that your data will perform
well and then since you're doing very
good analytics you will require
extremely good Excel skills and some
programming skills we'll remember we
discussed about Python and R a couple
of slides ago
that is exactly what uber wants as
well and then experience with data
visualizations and extracting insights
from the data so this is basically their
core part of the job description and
then basic understanding of some
statistic techniques and the ability to
employing them in solving certain
business problems are the second most
important or requirements from a data
analyst and this in my opinion is again
a very good example of a data analyst
job description guys so you might be
wondering what the average salary is for
data scientist and a data analyst well a
data scientist gets an average pay of
120 thousand dollars in the United
States and over 15 lakhs per annum this
is the average numbers and then for a
data analyst it's around 90 thousand
American dollars in USA and about lamin
lakhs per annum guys so these numbers
have been squished bound because of the
lower threshold and the upper threshold
from the data found across various sites
but then even a data scientist can earn
up to 40 or 50 lakhs per annum and data
analyst can go all the way till 30 lakhs
per annum as well so make sure that you
check your relevant experience and you
have extremely good certification that's
so that you can leverage that and use it
to get a very high paying job regardless
of it being a data scientist role or a
data analysts role so here are my final
thoughts on the video so basically I
have this quote which is very nice and I
thought I would share it with you guys
so it says it is very easy to lie with
statistics but it is extremely hard to
tell the truth without it so this quote
is from a person called Andrejs
Dunkels and I love the quote guys so what
do you think about the quote and again
at the end of the day we also know that
data scientists roll at the data analyst
roles are one of the most trending jobs
in the world which are present today
guys
ok guys a quick info if you want to do
an end-to-end data science certification
intellipaat provides a data science
architect master's course and those
details are available in the description
ok guys we've come to the end of this
session
I hope this session was helpful and
informative if you have any queries
regarding this session please leave a
comment below and be loud to help you
out thank you
