• Course by IIT Professors

  • 72 hours of self paced videos Lessons

  • Lifetime Access

  • Globally Recognized Certificate from GUVI

  • Dedicated Forum Support from the Instructors

  • 100% Online Learning

  • Access to community of over 10,000+ Students and Enthusiasts

About instructors


Mr. Mitesh M. Khapra

Assistant Professor

IIT linkedin

Mr. Pratyush

Assistant Professor

IIT linkedin

Fee structure




Students enrolled in schools/colleges without any prior work experience and faculty members of colleges and universities.


Applicants must provide his/her college registration number, these details will be printed in your certificate and cannot be changed

Fee Structure







Working professionals and those looking to up-skill


No pre-requisites

Fee Structure





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We are providing 7 days refund.

Our Learners

"The Data Science course was handled very well by the IIT Madras Professors. They've put enormous effort to make us understand this highly technical course and answered the doubts of all the students. I recommend this course."


"I took the Data Science program, which consisted of multiple classes. Overall the teachers knew the subject and covered what was promised.I would recommend this course to everyone."


"The course design is excellent especially for beginners to study and understand the basic concepts in Data Science. The lessons and course material are perfect and apt for this course-level."


Course Syllabus

Descriptive Statistics (Part 2)

10 Lesson

2 hrs

Descriptive Statistics (Part 3)

16 Lesson

3 hrs


13 Lesson

3 hrs


8 Lesson

2 hrs


14 Lesson

3 hrs

  • Data Visualisation
  • Read Complex JSON files
  • Styling Tabulation
  • Distribution of Data - Histogram
  • Box Plot
  • Distribution of a categorical variable
  • Joint Distribution of two variables
  • Swarm Plot
  • Violin Plot
  • Multiple Violin Plots
  • Paired Violin Plot
  • Faceted plotting
  • Pair Plot
  • Boxen Plots

Visualisation (Continued)

13 Lesson

3 hrs

  • Data Visualization - Recap
  • Pie Chart
  • Donut Chart
  • Stacked Bar Plot
  • Relative Stacked Bar Plot
  • Time - Varying compostion of data
  • Stacked Area Plot
  • Scatter Plots
  • Bar Plot
  • Continuous vs Continuous Plot
  • Line Plot
  • Line Plot Covid Data
  • Heat Map
  • Summary & Task on open-ended visualisation

Approaching Open ended DS problems

6 Lesson

2 hrs

  • Pandas Recap
  • Handling missing data
  • Missing data with Pandas
  • Open ended descriptive statistics
  • Agriculture Example Part 1
  • Agriculture Example Part 2


12 Lesson

12 hrs

  • Why do we need Counting and Probability Theory?
  • Very Simple Counting
  • The Multiplication Principle
  • Multiplication Principle Special Case: Sequences with Repetition
  • Multiplication Principle Special Case: Sequences without Repetition
  • Example: A Different Kind of Sequence
  • Multiplication Principle Special Case: Sequence Length Equals the Number of Objects
  • The Subraction Principle
  • Collections
  • Collections (Some Examples)
  • Collections with Repetitions
  • Collections (+ multiplication principle)
  • Collections (+ subraction principle)
  • Summary

Sample spaces & Events

19 Lesson

4 hrs

  • Introduction
  • The Element of Chance (Nothing in life is certain)
  • A brief overview of Set Theory
  • Properties of Set Operations
  • Experiments & Sample spaces
  • Events of an Experiment
  • Axioms of Probability
  • Some properties of Probability
  • Example problems (Probability Theory)
  • Designing Probablity functions (as relative frequency)
  • Designing Probablity functions (equally likely outcomes)
  • Summary - 1
  • Conditional Probabilities
  • Examples (Conditional Probabilities)
  • The Multiplication Principle
  • Total Probability Theorem
  • Bayes' Theorem
  • Independent Events
  • Summary - 2

Random Variables

20 Lesson

4 hrs

  • Introduction
  • Random Variable
  • Probability Mass Functions
  • Properties of PMF
  • Disctrete distributions
  • Bernoulli Distribution
  • Binomial Distribution
  • Example (Binomial Distribution)
  • More Examples (Binomial Distribution)
  • Is Binomial Distribution a valid distribution ?
  • Geometric Distribution
  • Is Geometric distribution a valid distribution ?
  • Uniform Distribution
  • Expectation
  • Examples - Expectation
  • Properties of Expectation
  • Function of a Random Variable
  • Variance of a Random Variable
  • Properties of Variance
  • Summary

Distributions & Sampling Strategies

10 Lesson

2 hrs

  • Introduction
  • Continuous Random Variable
  • Intution : Density vs Mass
  • Uniform Distribution (Continuous)
  • Some Fun with Functions
  • Normal Distributions
  • Probability Density Function
  • Standard Normal Distribution
  • Sampling Methods
  • Experimental Studies

Distributions of Sample Statistics

17 Lesson

3 hrs

  • Introduction - Inferential Statistics
  • Distribution of Sample Statistics
  • Parameter
  • Sample
  • Why do we Compute Statistics ?
  • Estimate Population Parameters
  • Random Sample
  • Recap : Probability
  • Probability Space
  • What kind of random variables ?
  • What is inferential statistics?
  • Our Roadmap
  • Demo 01
  • Demo 02
  • Demo Problems
  • Exercise - Part 1
  • Exercise - Part 2

Central Limit Theorem

13 Lesson

2 hrs

  • Central Limit Theorem
  • Demo 01
  • Alternative version of CLT
  • CLT - Attempt at Proof
  • Implications of CLT
  • Computing area under N
  • Demo 02
  • Special Significance for N
  • Likelihood of sample mean
  • Super-Impose N
  • Approximating Distributions
  • Demo 03
  • Normal Approximation of Binomial Distribution

Chi Square Distribution

15 Lesson

3 hrs

  • Chi Square Distribution
  • Estimating E[S2]
  • Estimating E[S2] - Exercise
  • Geometric arguement
  • Algebraic arguement
  • Find Expected value of the error
  • Estimating Var[S2]
  • Distribution of sum of squares of standard normal variables
  • Distribution for N>1
  • k degrees of freedom
  • Variance of X2(k)
  • Recap & Statistics of S2
  • On to Experiments
  • Expectation of Proportion
  • Variance of Proportion


  • Certificate can be generated after completion of the course.

  • Certificate is generated for every course individually

  • Certificates are auto generated



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Frequently Asked Questions

Will I get a certificate on completing a course?
Yes, upon successful completion, you will get a certificate from GUVI. This certificate will be accessible to you.
How can I ask my doubts?
We will have discussion forums where you can discuss the topics covered and ask doubts. Your peers or our Course Instructors will answer the questions.
Is it 100% online learning or should I come in person for any specific course?
It is a 100% online self paced learning course and there won’t be any necessity for you to be present in person.
What is the refund policy?
Customer satisfaction is our first priority. If you are not satisfied with the course, send a mail to [email protected] with the reason for refund and your feedback on the course with the subject line "Foundations of Data Science – Refund” , within 7 days of purchasing the course. Your refund will be processed immediately.
How long will I have access to the course?
You will have lifetime access to the course content (videos, assignments, community).
Why the fees is different for Students and Professionals?
We would like to ensure that large number of students are trained to be able to contribute to the industry and solving problems for the country, so the courses are priced low for students when compared with professionals
Will there be any syllabus difference for Students and Professionals?
No, there will no difference in syllabus.
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