Hello Everyone! Welcome to RU buZZing!
I am Mohit Agrawal, And I welcome you once again
To this new Series, which is based on Data Science, Machine Learning & Deep Learning
Our today's video is divided in 3 parts
First part is, what are the types of students aspiring for Data Science
Second Part deals with Data Science Interviews -
what are the Expectations & in reality
what are the questions asked
And What makes a good Data Scientist?
And the third part covers
what is different about this course
that is not there in other courses
And what are we bringing new on the table
that you should watch this video
Will directly jump to the first section
What are the types of students aspiring
for Data Science
In my career, I experienced different motivations of people
Who intends to become Data Scientist
What type of students want to be Data Scientist
They are divided in 5 parts
Type 1 Aspirants or students are those
who think Mathematics is not required
Type 2 are those who think Programming is not required
Type 3 believe Complex Mathematics is required
Only toppers can do it
Someone who had good grades in Maths
Fourth type think they you get big package
Data Scientists receive big payments
And the last type think
Only Data Science, Machine Learning, & Deep Learning is the future
the future. The rest is not going to be relevant
So we will take a look at it one by one what is their importance
So let us take a look at Type 1
No Maths Required
I will explain with an example
We have to collect marks of 5 students
to find their average
So 5 students will enter the marks
10 plus 25 plus 25 plus 30 plus 500
Divided by 5
Average as you know is sum of all marks divided by number of students
So 590 divided by 5
so 118
Something interesting to note here
The Average that you have got
is more than 100
Since the maximum marks is 100
the Average of 118 is not possible
So Where is the mistake
You can look at it and find the mistake is here
where data entered is 500
Because students can enter any data
Because this is a small data set
So we can look and find 500 is the wrong number
But when the data set is very big
When you have 1 GB 2 GB file with data in it
Then you cannot look at decide what is right or what is wrong data
So the numbers that are far from the sequence
they are called outliers
Because the actual sequence will range from 1 to 100
But the entered value here is 500
Which is far from the actual range
So, this is a outlier
What is interesting thing to note
To find Average we need knowledge of Maths
If you don't know Basic Maths, how to find an average
Then you cannot find this number
Second problem is to find an outlier
we need knowledge of simple Mathematics concepts
That is variance & Standard deviation
Variance & Standard deviation
It is clear that we need Mathematics
These type of people are thinking wrong
Now we come to the second type of people
No programming is required
Now let us take a look
For example, if I tell you
That we have a file
And my file has marks of 1 lakh students
And I have to find
How many students have scored more than 60% marks
Then what we should do.
Either we have to read the file
After that we have to process it
to find how many students have put more than 60 %
how many have got 1st Division
And after that we have to store it right
So file processing needs to be done
Otherwise you have to extract all the values
And store it in array, or Linked list or any data structure
Then whoever has got 60 percent marks
or more than that
To calculate those values
And it has to be stored in a different data structure
So it becomes clear that programming knowledge is required
So even this section of people are wrong
I have shared these 2 examples because
The main objective of a Data Scientist
is to deal with data
Data can be anything
One can give you in this form
or in a file
So to process data we need programming knowledge
as well as mathematics knowledge
Third type of people think that
complex numbers or complex Maths is required
For Example,
For example, e to the power -x to the
power 2 to the power 2x-7-2
someone who can solve by looking at it
For example, -infinity to infinity
That person cannot become data scientist
These people are thinking wrong
And why is that?
Because
such complex mathematics is not required
in general life
What you will deal with Data Science Numbers
is basic maths - differentiation
Or integration
Very basic integration
For example, you have to find are under the curve
or you have to find slope, that kind of detail
In short, basics are very important, deep understanding
You do not need complex understanding
Fourth type of people think high package is there
Let us assume Data Scientist get more than
Software engineer
That is an assumption
But now I will present 2 cases
First a Software Engineer is very good
His experience is better, his skills are better
He is available with good grades
And there is a Data Scientist
He doesn't know about data
Doesn't know math, doesn't know programming
Now let us take a look
That Software Engineer's skills will be in more demand
or that data scientist will be in demand
We can easily say, Software engineer will be more in demand
That person is in demand who is good at his skills
We can compare a Good Data Scientist with a
Good Software Engineer
But we cannot compare a bad data scientist
So comparison factor and high package factor only comes
if you are a good Data Scientist
If you are a good Data Scientist only then we can talk
about package otherwise it is wrong
That every Data Scientist gets High Pay
or he gets High Paying Job, wrong assumption
Type 5, the last type people think
that Data Science or Machine Learning or
Deep Learning is the future
And nothing else will exist
So these type of people are thinking wrong
For example,
Operating System we all use
We use Linux or we use Windows
Machine Learning is not required everywhere
For example, for Website Design
Android Development
Everything will be required equally
But a Good Engineer will always be required
If you are not good be it in any field than
then you will not be required
So the most important thing is to be a Good Data Scientist
What are the required skills and what people expect
We will cover this in the next part
Now in this part we will see
To be a good Data Scientist
or what are the expectations in Data Science Interviews
Data Science or Machine Learning or Deep Learning's interview's
There are 3 Types of expectations
First is how good are your basics
Second is how experienced are you in Project or
programming
And third is Research experience
Now let us understand each one in detail
So the first is Basics
Basics covers three things
One is What Factor
Second is Why Factor
And third is How Factor
What deals with
How much you know about Topics
For example, Do you know the difference between
Artificial Intelligence, Machine Learning or Deep Learning?
Do you know the difference between over fitting and under fitting?
Do you know the difference between Data Mining and Machine Learning?
Because in both we use Statistics
Then what makes them different?
What is the difference between Data Warehouse and Data Lake
If both store data then what is different between the two?
What is the difference between L2 and L1 regularization?
These topics reflect on your basic understanding
That you understand details of different topics
Second aspect covers "Why"
"Why" is more depth and intuition based
so that reflects on your deep understanding about things
The there is pooling
Pooling is a concept of Deep Learning
So generally a question is asked "Why Pooling is required?"
Why is it needed?
Second, why is ReLU needed in place of Sigmoid function?
Both are activation functions for Deep Learning
So why is ReLU preferred. Why?
Why Mean Square Error?
SO errors are of two types for example
(y minus y hat) squared
We can find a error like this as well
And the second type is Mod of Y minus Y hat
So why do we prefer this one over the other
These types of questions, Why Mean Square Error?
Why Regularization? What is the need?
Why is it needed?
What happens if it is not used?
What is Drop Out? What is Padding? Why are they there?
So Why Aspect reflects on Depth of understanding
Third aspect is How?
This shows if you have used it in real life or not
And if yes then how you have used it?
For example How to remove Outlier?
Like I had given the example to find Average
In that 500 was the outlier
So how to remove it using the concepts of statistics
So HOW to remove that?
There are some concepts, like Interquartile range, and many concepts
that we need to use
So you can only answer HOW if you have used them in Real Life
Which Algorithm is important?
Like there is Classification & Regression, these are different problems in machine learning
There are many algorithms available
So one should use which one?
And HOW to choose?
Which one to choose HOW to know that?
That is HOW
How to analyze Features?
Which are good Features in Machine Learning
which are the bad ones
If you are new in Machine Learning
You are not able to understand Features it is not a problem
This I am telling for those who have already done Machine Learning Topics
And how deep is their understanding to understand?
How to use Transfer Learning?
Transfer Learning is a concept of Deep Learning
How to use that?
One has understood Transfer Learning
Also understood Why
But How, how will we use
Which layer we will use on
This entire understanding is very important
to crack a Data Science Interview
Basics is the most Important part
If you are not clear with this then you will find it hard to even qualify
Next part is of Project or Programming
What does this cover?
This covers in the following manner
You have understood these topics but
Have you implemented it in Real Life project?
If you have made a project and used these concepts
then it reflects on your strength
that you have a deep understanding
And you have used these things in Real Life
And the last is Research
Research is very important factor for any field
You may be Java Developer, or C++ Developer
You may be Web Developer
then you will do Research. Research does not mean only
to publish. Research is to have knowledge  about the latest topics
Do you know the latest topics?
What are the better features of Java 8 over 9
In C++ what are the new Standard Template Library introduced?
Similarly in Data Science what are the new Algorithms
Which new Algorithms?
Which are the new models?
Which are the new publication?
Which is the new technique?
There are many Research happening in different fields
What is the new Optimization?
If you have knowledge of all this
Then you can prove yourself better
And you will have deep understanding.& good understanding of things
So Research if you are applying for a normal Engineering position
Data Science Position
Then Research is not always expected of you always
This is optional
But if you have knowledge of Research
then you will get a upper hand
Some companies that work on Research will definitely give lot of importance to this
Besides this have you published something?
Publishing Papers?
So you don't have to be scared
We will take a look at this
But this is what is expected
That means if a lot of courses are telling you
that Maths is not required
Lot of them are saying that programming is not needed
Then definitely they are saying wrong things
So you have to understand all three aspects
to be a good Science Engineer
or to crack a interview of a big Data Science Company
These 3 aspects are very important as told
Now we will understand what is different
You may have done many courses already
Even in my Graduate & Under graduate days
when I started working on Machine Learning
Then even I went through a lot of courses
But a problem I faced
Some courses claimed that you need Maths knowledge
Some claimed Maths is not needed
Some claimed Programming is needed
Some claimed use Python Libraries
With all this I got confused
So this course is with this perspective
That it will also teach you from a interview perspective
It will not just cover basics
After covering basics
it will show how to use those in interviews
How are they asked in interviews
How to crack those interviews
We will be covering that aspect as well
Definitely if you follow this course
Then I give you 100 percent assurance
that you can crack any Data Science Interview
So if you what to be part of this course
So please like, share & subscribe us
And also share it with your friends
And come let us together explore Data Science Thank you :)
