Master Probability & Statistical Inference for Data Science with R | NPTEL Course
Course Details
| Exam Registration | 207 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Mathematics |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 18 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Essentials of Data Science With R Software-1: Probability and Statistical Inference
In the world of data science, the ability to extract meaningful insights from raw data is paramount. This process is fundamentally rooted in the principles of probability and statistical inference. A new 12-week course, Essentials of Data Science With R Software-1: Probability and Statistical Inference, offered through NPTEL and taught by a leading expert, provides a comprehensive foundation in these critical areas using the powerful and popular R software.
Why This Course is Essential for Data Science
Any data analysis is incomplete without statistics. The core objective of statistical science is to draw reliable conclusions about an entire population based on a small sample of data. This is impossible without a solid grasp of probability theory and statistical inference. With the rise of data science, learning these tools from a computational, data-driven perspective has become crucial. Misapplying these fundamentals can lead to incorrect conclusions, making this course vital for anyone serious about data analysis.
Meet Your Instructor: Prof. Shalabh
This course is led by Prof. Shalabh, a Professor of Statistics and Data Science at the prestigious Indian Institute of Technology (IIT) Kanpur. With over 25 years of teaching and research experience, Prof. Shalabh is a renowned authority in linear models, regression analysis, and econometrics.
His distinguished credentials include:
- Authoring over 100 research papers in national and international journals.
- Writing four books, including a seminal book on linear models co-authored with the legendary statistician Prof. C.R. Rao.
- Authoring a book on Statistics with R software that has been downloaded more than 5.4 million times.
- Developing several web-based and MOOC courses for NPTEL.
- Receiving numerous national and international awards and fellowships for his contributions.
- Playing a key role in propagating the knowledge of R software across the country.
Course Overview and Structure
This is a 12-week course designed for undergraduate/postgraduate students and working professionals. It systematically builds your knowledge from the ground up.
| Week | Topics Covered |
|---|---|
| Week 1 | Introduction to data science, basic R calculations, and probability theory |
| Week 2 | Probability theory and random variables |
| Week 3 | Random variables and Discrete probability distributions |
| Week 4 | Continuous probability distributions |
| Week 5 | Sampling distributions and Functions of random variables |
| Week 6 | Convergence of random variables, Central Limit Theorem, Law of Large Numbers |
| Week 7 | Statistical inference and point estimation |
| Week 8 | Methods of point estimation of parameters |
| Week 9 | Point and confidence interval estimation |
| Week 10 | Confidence interval estimation and test of hypothesis |
| Week 11 | Test of hypothesis |
| Week 12 | Test of hypothesis for attributes and other tests |
Who Should Enroll?
INTENDED AUDIENCE:
- UG students of Science and Engineering.
- Students of humanities with a basic mathematical and statistical background.
- Working professionals in analytics and data science.
Prerequisites
To get the most out of this course, participants should have:
- A mathematics background up to class 12 level.
- Some minor statistics background (desirable).
- It is preferred (though not mandatory) to have completed an Introduction to R Course. A relevant NPTEL course is available: Introduction to R Software.
Key Learning Objectives
By the end of this course, you will be able to:
- Understand the fundamental concepts of probability theory that underpin all statistical analysis.
- Work with different types of probability distributions (discrete and continuous).
- Comprehend core inferential concepts like sampling distributions, the Central Limit Theorem, and estimation.
- Perform point estimation and construct confidence intervals for population parameters.
- Formulate and conduct statistical tests of hypothesis to make data-driven decisions.
- Implement all these statistical techniques computationally using R software.
- Interpret R output correctly to draw valid statistical conclusions.
Recommended Textbooks & Resources
The course is supported by a robust reading list, including a key text by the instructor himself:
- Introduction to Statistics and Data Analysis With Exercises, Solutions and Applications in R - Heumann, Schomaker, Shalabh (Springer, 2016).
- Applied Statistics and Probability for Engineers - Douglas C. Montgomery, George C. Runger (Wiley).
- Introduction to Mathematical Statistics - Robert V. Hogg, Allen T. Craig (Pearson).
- Probability and Statistics for Engineers - Richard A. Johnson, Irwin Miller, John Freund.
- Mathematical Statistics with Applications - Irwin Miller, Marylees Miller (Pearson).
- The R Software-Fundamentals of Programming and Statistical Analysis - Pierre Lafaye de Micheaux et al. (Springer, 2013).
- A Beginner's Guide to R - Alain F. Zuur et al. (Springer, 2009).
Industry Relevance
INDUSTRIES SUPPORT: The skills taught in this course are in high demand. All industries with a Research & Development (R&D) or data analytics setup will find this knowledge applicable. This includes sectors like finance, healthcare, e-commerce, technology, manufacturing, and market research.
Mastering probability and statistical inference with R is not just an academic exercise; it's the first major step towards becoming a proficient data scientist or analyst. Enroll in this course to build an unshakable foundation for your data career, learning from one of India's most esteemed statistics professors.
Enroll Now →