Data Science. As you know,
you will come across three different areas
when it is data science. One is software programming.
Second one is statistics.
Okay.
And the third one is algorithms
okay
for all these little things
but when you're when you're talking about
tools in data science I'll say that also when
you're talking about tools in data science,
you are having three sets of books are Python
answers.
Okay? Okay.
Okay.
You're learning data science, you will be
able to go with two different designations
oneness as a data analyst.
and other one is a data science
okay. So, when you're dealing with data analyst
position, you have to know some things related
to programming
and statistics
many are moving as a data scientist who should
know how to analyse the data,
statistical
concepts and machine learning
that is over
okay that is machine learning
Okay.
All these things whether you go for a data
analyst or whether you go for data scientists
these are the tools
when
Making the art
you may not have any prior experience in programming
languages if you don't have any technical
knowledge also
I will be a little bit easier one it is a
statistical programming language
okay
even though if you don't have any idea or
programming languages you don't have any idea
regarding the school also it's not a problem
right from the beginning only we will be moving
on right from the structure will be understanding
how to use the syntax all those items very
straightforward. The programming language
is very straightforward. It has a number of
packages for even for statistical information
also I'll come to that part what are the statistical
things that you will be learning it in data
science that also is it so that it will be
easier for you to
like you can make up your mind altering that.
So our needs it is not at all a problem.
If you don't have any prior experience in
programming also it is not a bigger deal.
But when you are talking about Python, it
is something called object oriented programming
language.
The syntax is a little bit complex.
So if you are going with Python, definitely
you should have some idea of prior languages
like C, c++ or Java or some kind of basic
knowledge you should have in those things.
I'm not saying like compulsively have a capacity
to learn this particular Python language it
is fine. Because it is logical and simpler
disagree a little bit complex.
Right now only this developing.
Python is there for the development, other
processing option, but when it comes to data
science and communication options right now
only we are entering Python so a number of
practices are there and it is very helpful
when it is more powerful than
But the problem in Python is the syntax is
a little bit new.
So people might be finding it difficult from
a non programming background
or from a non programming background. If you
are taking Python is it will feel a little
bit
but I'm not saying like you cannot cope up
with that you can do it but you have to do
a lot a lot of hard work.
Okay, that is for Python. The next tool is
SAS.
SAS is something in an article
analytical and statistical
which is entirely different from R and Python.
Even if you don't know any programming knowledge
also no problem.
It will be very, it is entirely a visualisation
process.
analyse it eventually, and then form your
scripts, but you have to memorise certain
conditionals but it is very straightforward
even if you don't have any prior knowledge
related to programming also no problem since
you work
I mean they will do me
Yeah, yeah. So tools will be very helpful
are in SAS will be very helpful but I will
recommend Python for you.
When you plan to go for your job opportunities,
you can straightaway look out for domains
such as banking, financial investments across
our insurance across those areas, you can
take a look and look for your job applications.
Because banking financial sectors are highly
made of data centres. So people those who
know are in fire fast they are very very welcome
inside their sector because Python needs a
little bit programming is a little bit complexities
there. So people from India that
They want to jump into this particular data
centre which I'll be recommending our instance.
That is your wish, you can go with either
R or you can go with SAS, SAS is entirely
a different concept. Most I mean, it's an
analytical tool everywhere they will be using
this analytical tool, both analyzation and
you can go for your
visualisation aspects. Also, if you're working
in a retail marketing, if you're working in
any kind of sales team or if you're working
for a banking financial institution, everybody
will be able to use this as
their kind of analytical and statistical.
But what is our biggest advantage is that
we have any packages
entirely available in the studio exercise
itself, there are
three options. So you'll be able to do okay.
But
you have to understand the requirement and
you have to write the syntax on your own
But it'll be straight English.
There's no separate genetics and all over
here to memorise and if you understand the
entire requirement and it will be good, okay,
and ask for your last question that you want
me to answer that whether it will be okay
for you to enter into this kind of environment
after doing your MBA and after your previous
experience,
I will be recommending data sensors of this
country because in future in for another 20
years or 30 years or two that will be some
kind of improvement in data science, but the
platform will be same one
many different tools may come up in future,
but the base platform will be going to be
the one of the best
platform that you will be using. I mean it
comes comes at a big data. So, one part of
the data is how to another part of big data
will be data sense. So, right now we are entering
into always more data
Whether you work in any industry to take banking
finance, investment, risk management, fraud
detection, or if you're planning to enter
into crime investigation or whatever it is,
whether it is media or it's going to be education,
voluntourism whatever it is everybody has
got millions and millions of records with
them.
Everybody wants to process their information
to help the business by helping the business
only we are entering into the stylization.
Let us take an example you are creating you
are planning to create you're planning to
run your own business and you want to know
what will be the profit for your future. For
next two months, we want to find out the profit
for an experience. But was it possible for
you to find out for next two months we could
have the past information past data to predict
the data you analyse it you will try to visualise
it and then you will come to conclusion that
this will be the profit for next
So data plays a very important role.
near future definitely year after year the
data is going to get increased one is not
going to get difference.
If you are into an entirely into an IT car
loan, I would have said this to you before
I ask you to take our advice on since you're
not into it background of your income, your
different background, if you're going this
is your wish you can go today there are or
you can go with SAS Anything is fine. Or both
options you can pick that depends upon your
interest. Okay. In all these three things,
the common area is statistics.
So to understand statistics, there are certain
things that you have to make an army like
you have to go through that. It is not like
everybody is not from a mathematical background.
We all did mathematics when we are younger,
like we were doing our college first year
or second year. That is
You have done some mathematics even in your
bachelor's degree you have done some stats,
it's all
but later after that we won't be having much
I mean after completing our first year or
second year and then after five years later
if you're asking like what statistics on what
is we have to do with that means we are not
going to create any information from this
and you're not going to derive anything on
all this our Python and SAS they all have
a package.
For statistics, amazing that you have to understand
about statistics is mean. Median,
standard deviation,
variance,
covariance,
frequency, cumulative frequency
range minimum
maximum
regression
correlation
probably
random variable
normalisation
crisis square this
hypothesis testing,
trying to do some analysis
that's these 
are the things that you have to be very very
familiar with.
Then we inspire era
means quite error
related.
But I won't say like everything will be used
in one single analysis or one single page
all these things are common, you will have
it in a package as well as the different packages
for all the seats. Python has got one single
package for all these things as is having
its own syntax for all these things.
So, according to the data set open, however,
you are taking, you have to create an ami
like after seeing the data set you will know
whether to find the mean value or whether
to find deviation of that particular data
set or whether to find a particular variants
of that variable. So all these things when
you are trying to find out sorry, when you're
trying to analyse it has to be able to understand
that option. So in that case, you want to
come across either of these things every time
it is not like oh
All the times it is not like we are going
to be the mean calculation median
depends upon the data sometimes in a data
set, there won't be See, if you have a numerical
value when we will be able to calculate mean
median mode or not
right, there is no numerical value let us
take it as having only character Well, it
was impossible for you any statistical analysis
practice not possible. So, it depends on the
data set on So, understanding the way on index
This is what statistics is this is why it
is printed up in our tools also. This is the
package with the practice also go with these
kinds of statistical information. And one
more thing is that you are not going to be
not going to see how mean has occurred, how
median has come? What is the normalisation
but formulas are there you have to memorise
that. There's no other way to memorise a formula,
we are not going to be right
here I'm going to say what is the probability
Now normally this would maybe be a new baby
step.
Python distribution is a binomial distribution.
But we are not going to see how these all
things came up. We are only able to do that
one because it's the highest statistical information,
we don't want that we will be dealing with
our data saying statistical concepts. So,
how to leverage these practices, how to use
these packages, what are the different packages
available, where when to use it, all these
things really these are the three strategies
to understand when you are dealing with these
basic strategies. These are obvious, okay.
And finally, under mission Ansari data sets
you will come across as called as machine
learning algorithms for prediction.
machine learning algorithms, where you will
be going on with prediction with the help
of this algorithm.
and here also the same thing I'm saying about
algorithms I'm talking about me doesn't mean
that
You want to create algorithms, there are some
things, you have to take the data set, divide
the data set, and then go with the algorithm
perfect algorithm, you have to find it. Because
the industry is using about 15 algorithms
right now. All types of industries, anything
not only one industry, or your retail marketing,
for weather forecasting, you're
in the media or entertainment industry, banking,
industry, insurance, investigations, prions,
all these industries all I mean, like cyber
industries, social networking, all these e
commerce platform every day, everything is
something 14 to 15 hours.
I'm not saying it's either algorithms widely
available, we have years of algorithms available
in Python or not, but only 15 algorithms are
currently running in the market and they are
very very popular.
In the industry also costs will be 15. And
so when you are dealing with this will be
hybrid.
If you have to know how this data set properly
that is something very important. So you're
not going to create any algorithm. You don't
want to create any other already algorithms
get the data set, analyse it, understand what
you have to do do some statistical analysis
for that one, then come with your production
option, that is find out which are violent
movies. So finding out the algorithm to use
that algorithm, the technique of understanding
that algorithm only we will be seeing in our
course, how to understand it, how to how to
take it and then fight them, how to get them
what it all highlight, I want to be honest
with you, it's not that it's not like that
once you finish up this course, we'll be able
to do all sorts of data. Anyhow, we will be
taking different data sets only since I'm
working in that field. I'll be giving you
different data set for you to practice and
different data set for our regular practical
session. Also, we'll be doing
But doesn't mean that you will be able to
read these data set Monday you will be given
there are a number of data set, but you have
to the techniques are exactly the same, the
data set will be different. So, to put that
technique into that particular data set about
forgiveness practice what we have to practice.
So, many you have to go with the article understanding
skills, problem solving skills, analytical
skills, these are the key skills that into
this run away, there is no need to worry or
that you are using.
So, to make it easier, there are two tools
that you can choose either you can choose
R or SS and one more thing also is it r means
you will go with
both data analysis,
analysis
and machine learning concepts because packages
are available
for machine learning algorithms, whereas fast
means hopefully we'll be able to
To do one live data analysis practice,
all types of analysis will be able to do this
because machine learning is not used the current
SAS to do if you want to go with the machine
learning also in SAS means you have to go
with SAS.
Software currently is used, but it isn't the
beta version. Still it is not been released
to the market. It is testing the
final release. So initial learning with SAS
if you're applying that will not work out
with SAS, SAS, but data analysis visualisation
an entire concept of assets liquidation status,
all type of analysis you can do with SAS,
only one user is not able to lose at this
prediction option. You won't be able to but
entirely What are things are cannot do in
analyzation your size to be able to do that.
That is very begging are we going with the
data analysis and machine
They have their own packages under the help
of the tracking
programme I mean in your data set and give
this
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