Hey everyone and welcome to the section in this section.
We are going to work with data science that what actually data science and what are the technologies
used in data science.
So before that this one is a little video about what actually Data Sciences.
So here I have an image downloaded from Internet which is showing a we can see that big bag of data
science.
And let me begin with the definition of data science that would actually is data science so data science.
Most of the people think that this thing is known as Data Science graphical plotting and visualization
of data.
And this consists of like something analyzing the data and loading them on graphs or let but this one
is just a part of data science and data science is actually all these steps in forming these graphs
and using these graphs that they are use a common example is like you have what do you do.
And you have also like used Facebook Instagram.
Then there you see something like when there's a post or like this video here.
This video has fifty four key lights and sixteen K dislikes and a number of comments.
So these things are actually data you people just make the video like or dislike.
But the program or the person that are sitting at the back and analyze their data like this thing is
getting so much like this thing is getting popular.
This thing is not going to work in the future so they generally analyze these data that what is the
demand of market and then they perform according to their data.
Make these changes and provide efforts and they see that how that thing is affecting data on company
a more common example is something like Let me consider a state with not a state a small city that consists
of 1000 people.
So I have a company on mobile not me just take two example like Apple and my Apple provides expensive
mobiles that are just like nowadays in thousands of dollars like iPhone access of Trump and another
company that is and my providing cheap mobile it's also there just 150 bucks now in the small city of
thousand peoples I put the data scientist know what they go for.
Like is first they collected data and hack in need of data science that is these graphs.
You can also search for them the data Santiago needs.
And this consists of all the steps in data science that is first to collect the data like these thousand
people just assume that nine hundred people are not much reached digits on 200 to 300 dollars a month
or maximum five hundred dollars a month and digest under are upper middle class people or we can assume
that the upper class peoples who are earning thousands of dollars.
So if we talk about the mobiles then only these hundred people that are in upper class can afford that
mobiles.
Person who is earning 500 or 300 dollars a month cannot afford a mobile phone thousand dollars.
So this is a data like the company will analyze the data that what the market is that what they demand
of people invoke according to them like the.
And my company.
And this is one more complaint that we can take example of that is Nokia as you can see nowadays Nokia
has lower market than and might that is because Nokia once focused on Windows Mobile.
That is something people did not like but they did not change according to the need of market.
They just go with the product they are making their product superior and superior.
I believe that at the time Nokia was a good but having a good product and having a need of product.
Is a different thing.
There are some products that are very great and we can see that just ideal products.
But there are no market for them.
Sometimes you have seen and maybe some of you have also practically feel this thing that you are an
enduring or technical type of student.
Sometime you have posted videos and tried to post a new video on YouTube that is related to some particular
concept like some post like hacking something hacking the password of any VI file something like this
and you just get 100 or 200 views.
But on the same side any against incident just like funny video or any moment both just leave you know
something shit that doesn't have any meaning and doesn't have any sense.
But that entertain people and that video gets millions of views so that's the thing is that we in data
science just not focus on developing the product.
This is the misconception students that data science is something by virtue and by learning which we
make like products that are generally ideal and no other product stand in front of them.
There is no data sciences.
Now we are taking the example of like a thousand papers and just assume that MRI was the company at
that time.
Like am I starting in 2010 then this one is just example not practical and that has happened before
like the MRI has observed that out of thousand people they put any person to collect all the data and
after all these thousand nine hundred can afford the cheaper buy sold the company decided to make the
cheaper price maybe the margin is come down a few dollars on a particular mobile but this is a very
high the demand is very high and this thing is really good that out of thousand people 900 peoples are
preparing their product.
These things make the company market best because in the top on the list now it would talk about collecting
the data then these are the steps in data sites like first we collect the data and then we store that
data collecting means that generally collect all the data like a thousand people just assume we have
thousand fifty then we will store all the data like in a database First we used to store the data notebooks
you also has in store at any point in your life some data on notebooks when by and you did not able
to use mobile it's all you when you do not have mobile it's like when you are playing something with
your friends or you are selling something you had just written on notebook that this is I have done
you also sometimes make a list of these routines that you follow daily that's what also the data that
you have working on that data makes you like if you follow up of that routine maybe you will have a
perfect body perfect shape Perfect job perfect mind then that thing that you are analyzing all the needs
that you require to be perfect and working on that.
So similar that is in case of computers.
Now the data collected like the thousand and 50 then the next step is cleaning the data like maybe out
of that were hundred and 50 fifty of children's that do not have money.
They do not.
They cannot even just afford any of T-Mobile.
So companies will never take all the data.
They just take the data that is under the market like those who are earning those What about rating
then the company analyze the data that was the demand of the market like 900 can afford a cheap one
hundred can afford the expensive one so that the analytics after that testing experimentation and algorithm
then they design something like the things that can affect their company in positive they like on their
demand they are producing cheap mobiles.
They decided to make cheap things cheap items like again the example of MMI and may provide cheap mobiles.
The margin is low on their mobiles.
But what they did they stop providing earphones that they might earphone is a product that just consumed
nearly 10 dollars for every piece.
So they removed that one to increase their product because their margin is already low.
But they provided the product according to the market.
They never provide like in thousand dollars for their mobile.
They just saw the market that what is the nature of market and what is going to work on that market.
Not like just produce anything identical.
Example of YouTube videos.
There are some perfect videos but did you not get the view because there is no market for particular
of that.
Like if you are again a technical guy posting a video of some technical stuff then if you consider what
led people out of that leg maximum.
There will be 5000 technical persons that are belong to some like marketing comas arts fields.
Some are like old person and some are children.
So the market for technical stuff is only 5000 but out of that full leg I believe ninety thousand pepper
entertainment.
They all need entertainment and something they can be happy for so they vote and like 60 day days.
Now this thing is not part of all like data scientists.
This thing all is data science.
But now we are talking the jobs of data science at present data science is one of the best feel for
we can see that salary and for job purpose data science is on the top of the jobs and have highest paid
salary Artificial Intelligence in deep learning is going to be better than that one.
But at present they are not like the data science and other things because they are not just so advanced.
This maybe in 2050 or 2030 that will be on top.
But now we have the data.
Now in this hacking this thing first but it is collecting the data is the vocal software engineers that
is the most of the students in computer science or computer engineering go for if they do not learn
about the data science and do not focus on data science they will remain on this first step collecting
the data and collecting the data doesn't mean like going to person in person writing things making notes.
This is something like here.
Again this video collecting the likes and dislikes data from the program algorithm they collect this
data and work on that data.
If Dale likes are you know very high then the video became on trending.
If the views are increasing so fast the video again beyond trending.
And after that this part that is gleaning and storing that data like the example of 2050 peoples then
storing that data and cleaning their data is the part of data and.
This one includes the data engineers and this one that I am telling you is nearly most of the companies
prefer this thing.
They provide software in for collecting data and for storing and cleaning the data we have great engineers
because data scientists are paid higher than these two.
So but these two works did not require any logic.
Logistically collecting data and storing the data which an engineer can do so they just take this data
scientist for these two steps.
That is for analyzing the data.
Like now the data is formed Clean and stored.
Now we have the list of thousand people now they will analyze that how many of them required this thing
and acting like nine 900 require that 100 require that then they will analyze the data in the steps
and make a graph like this one like this one.
Like here we have 900 people refereeing the cheap mobiles and hundred are pressuring the expensive mobile
long look at the complete one but just focus on these two.
So they will make the visualization so that every person can understand that.
And the graphical visualization is the best thing like if you have noticed this thing I think most of
you have seen these things these shows.
This one is example of customer analyses but you have seen the growth of companies like Apple Amazon
in these kinds of graphs.
So you cannot just write the name of like here we have fourteen thousand four hundred seventy four.
Here we have twenty five thousand and forty three.
You cannot just write names of everyone.
So there will be a there must be a V by which you can represent all the data that is done with the graphical
representations.
People use these graphical representation by for defining all the needs and they have their x and y
axis according to their needs.
Like if we are talking in lax of millions then these things will be millions like 1 million 2 million
3 million.
If we are talking in general numbers then they will be normally like here we have a difference of 50
on by X is difference of 20 on x axis.
So that's what the visualization is.
Then we have the testing the thing like.
Now you have analyzed the data now what to do with that.
You just don't analyze the data without any need.
So in this step the data scientist implied that data in such a way that the company growth will increase
like again the example they provide cheap mobiles so cheap mobiles so their market is going to be increased
because most of the people are going to people that know the about stuff that is a and deep learning
is also this type of data science.
That is something like.
And one more thing.
Whenever you have noticed like in YouTube videos in medias you have noticed that men never talk about
data science.
The artificial intelligence become the machine learning comes there.
So people get this one also has a misconception that science is about machine learning and out of the
agency.
Yes that one is also about that but not only for that machine learning and artificial intelligence see
are just applications of data science.
If you want to develop your A.I. and want to go for machine learning you must have to learn the data
science because without data science you cannot work on that because machine learning what actually
is machinery making the machine learn something by itself and how the machine will can we learn learn
anything so this thing is a common example like if these are businessmen then there's nothing like he
got and got a product and this will.
Does that product will work and that will book shortly.
The thing is he analyzed the data.
He looked at what the need is like in a fruit market.
If I sell a soap maybe it will not work because that one is a fruit market.
But if I go for like proper fruits fruits without chemicals and good looking fruit like fresh then maybe
that will work.
So that is the thing is machine learning is something like a machine just learns from all the data that
he has.
It has like a washing machine that what it has to do with that particular thing it has to provide this
so that so.
So according to the clothes that are in the lake natural water in the washing machines.
So they're just a part of the assets will be considered application of datasets that the machines just
analyze the data and work according to data and to analyze the data.
That machine also need to work with data since without the data the machine can never learn by itself.
It's not something like they understand everything by their own.
Even we humans we did not understand or we cannot learn everything.
BI But when you start to work you just noticed other people's walking you analyze that thing that people
are walking with their legs and that's the particular thing like you don't know how to walk when you
get but like when you are six to seven months old you do not know how to walk maybe if someone tell
you maybe you think that by a head I can vote.
But that's not possible by hands you can also walk.
That is another thing but you do not walk with your hands because the legs are made for them.
And how did you get another leg are for that.
By analyzing other peoples that they're voting and using their legs.
So that's the thing.
How the machine learning works and what the machine learning actually is.
So do you go for machine learning you must test to go for data science first and then for Dedmon And
after that what we are going to do in this section we are going to work on these two parts.
We will have a predefined data and that is also clean and stored on an already structured in a way.
We will just use that data to analyze that data inform of these beautiful graphs like these things.
This thing is something like you cannot imagine that how you can make this one without programming and
this one is also not something like if the focus on this one so you can just imagine too broad this
thing here this thing without reprogramming.
You also cannot do this thing so we are going to learn in this section that how we can analyze the data
use that data and visualize the data in a way that you can analyze that data in this step sorry bug
efficiently on their data in that step.
Like if you made a graph and this one is something any product that have maximum demand then you will
in this step before making this product in large quantity than the others so that this will increase
your growth.
Not like just make any product.
So we will learn all these things that how you can get the data how you can analyze the data and how
you can work on data that data.
So that efficiency will increase.
I think now you are getting interested about dissection because learning these things data science machine
learning.
I believe this is a dream of every software engineer and every computer science student even I am in
Python.
If I say by about Python my most interesting like them feel that I am most interesting is the data science.
Even then the machine learning because machine learning is something that requires a number of calculation
in different ways but that also work on data sent by analyzing the data.
And this one is going to be an interesting one for you also.
And this is all about the data science what the data sciences and the jobs in data science coping and
data science and how actually all the things work.
I hope you go and get a little idea module you get when we go to all these actions.
Now this picture is also small because of that blog data diagram for the data science that generally
someone first prepare data like by cleaning the data and transforming the data then they will like store
that data and analyze and make the models then they visualize that more does to the community and that
what is the demand what is the need and then they deploy that all these things.
So in for that what this video I hope you got the idea.
So thanks for watching.
We will continue from the next module the name by.