Hello and welcome to this new section which is data analysis using Python.
So in this section we will learn how to perform data analysis on a set of data.
So this is an intellectual when we will learn what is data analysis and why Python is actually used
to perform data analysis.
So let's first go ahead and understand what actually data analysis is.
So the simple definition of data analysis is analyzing data from multiple sources to extract meaningful
information.
So in data analysis what we do is that we don't mind some data source which we have to analyze and that
is let's say you have a website or an e-commerce Web site which is going to generate tons of data on
a daily basis.
So if you want to analyze that data you simply extract data from that source you perform data analysis
on it using some programming using some language like Python.
And then after doing a data analysis you get some meaningful information which you could use to make
better decisions.
So that is the basic and the most simplest definition of data analysis.
And obviously we could go into more complicated stuff.
But for now this is what the data analysis is.
Now let's learn why didn't Alliss is required.
So the first and foremost reason is that it helps us make better decisions.
So a lot of organizations like let's say the e-commerce Web site Amazon has to make business decisions
on a daily basis.
So these organizations actually rely upon the data which they get.
So for example if you have a e-commerce Web site like Amazon there are going to be a ton of users visiting
your Web site on a daily basis and what they are going to do is that they are going to generate a large
amount of raw data.
Now this can actually be analyzed and information could be drawn from that data and that helps Amazon
or any e-commerce website for that matter to make better decisions.
And the next most important thing is best use of available data.
Now not only Amazon but any data intensive organization is going to produce a large number of data in
the backend and throwing away that data does not make any sense.
And thats why this data is actually analyzed so that some conclusions could be drawn from that data
which could be more valuable to the organization.
So this is the main purpose of data analysis and there would be a lot more.
But the basic purpose of a data analysis or the basic significance of data analysis is to help make
better decisions for organizations as well as to make the best use of the available data.
So why data analysis is the future.
So what Ive done here is that I've laid out the basic set up of an economist company or you could see
e-commerce Web site and we will learn how data is used by this organization.
So as you could see we have the e-commerce Web site at the center and on the left hand side you could
see user.
So what this user actually does is that he visits the e-commerce Web site and he basically browse through
or goes through the commerce Web site he might view certain products he might buy certain products and
he might carry out a bunch of activities on the e-commerce Web site.
So what the e-commerce Web site actually does is that it generates a real user data that is nothing
but the user's browsing history.
It also stores the products viewed by the user.
It also stores a bunch of things like which particular product they use bought and everything like that.
So every time a user visits on an e-commerce Web site or our data is generated at the back end.
And just imagine this thing with a big site like Amazon.
So there are going to be a million users visiting per day on Amazon who generate raw data.
And with this what we could do is that we could perform data analysis on the raw data and we would find
certain things like which product is being sold and which category or actually the most viewed products
by the user which are the trending products which products they use are not buying and everything like
that.
So you could use that information that is let's assume that from the data analysis we came to know that
Apple products are being sold most in the mobile category so this information can be used to make decisions
like OK as the users are buying Apple products in the mobile category.
Let's place that product at the top and let's place the other products at the bottom.
So in such a way that raw data can be converted into useful information using data analysis and later
that information can be used to make logical conclusions.
And depending upon those conclusions we could optimize e-commerce website.
Now this was one of the most simplest example of data analysis and obviously Ive always simplified things
here.
A lot of them things actually happen in the back end but this is what generally happens when you perform
data analysis on the raw data.
So now the next thing which we will learn is that why Python for data analysis.
So actually there are a lot of options to perform data analysis you could use tons of tools or tons
of other languages as well.
But the reason for using Python for the analyst is that its faster as compared to other alternatives.
Now when you talk about data one obvious program comes into mind is Excel and Excel is actually good
at analyzing data but Excel is only suited when that data is ready less.
So let's see if you have hundred rows of data that's ok you could analyze that with Excel but then you
have a million user data like Amazon or any other e-commerce Web site for that matter.
Then using axil makes no sense because it's obviously too slow and there are not a lot of tools in Excel
which you could use to perform meaningful data analysis.
So Python is comparatively much faster as compared to excel or the Internet.
And that's why Python is actually used to perform data analysis.
The next most important thing about Python is that it has a simple syntax.
No you won't if someone is not a programmer or if he's not from the computer science background even
then he could learn Python because it has a simpler syntax.
And that's another reason why most people prefer Python for performing data analysis and the most important
and the last reason for using Python for the Donnellys is that it has multiple libraries for data analysis.
So Python actually has a bunch of tools or a bunch of options for the Daniels's like by Bandar's and
everything like that.
So we're actually going to learn about non-plan pandas and the upcoming lectures as well.
So this is the main reason why we choose Python mostly for data analysis.
So that's it for this lecture and hopefully you guys will do understand what the time analysis is how
data analysis is performed and what is the significance of a data analysis and also why Python is chosen
as the best language for analyzing data.
So thank you very much for watching and I'll see you guys next time.
Thank you.