NPTEL Course: Sampling Theory & Linear Regression with R | Prof. Shalabh IIT Kanpur
Course Details
| Exam Registration | 53 |
|---|---|
| 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 | 19 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master the Statistical Foundations of Data Science with R
In the world of data science, the ability to extract meaningful insights from raw data is paramount. While modern machine learning grabs headlines, the classical statistical methods of sampling theory and regression analysis remain the bedrock of reliable data analysis. The NPTEL course, "Essentials of Data Science With R Software-2: Sampling Theory and Linear Regression Analysis," bridges this crucial gap, teaching you how to apply these fundamental tools using the powerful, free R statistical software.
Taught by the renowned Prof. Shalabh from IIT Kanpur, this 12-week program is designed to provide a deep, practical understanding of how statistics drives data science, moving from theoretical concepts to hands-on implementation.
About the Course Instructor: Prof. Shalabh
Learning from an expert with both academic excellence and practical teaching experience is invaluable. Prof. Shalabh brings over 25 years of experience in teaching and research at IIT Kanpur. His expertise in linear models and regression analysis is globally recognized, evidenced by his seminal book co-authored with the legendary statistician Prof. C.R. Rao.
He is a pioneer in propagating R software in India, having developed widely accessed MOOC courses on NPTEL. His book, "Introduction to Statistics and Data Analysis With Exercises, Solutions and Applications in R," has been downloaded over 5.4 million times, a testament to his ability to demystify complex topics for a global audience.
Who Should Take This Course?
This course is meticulously designed for a broad audience seeking to solidify their data science foundations:
- Undergraduate & Postgraduate Students in Science, Engineering, and related fields.
- Professionals in Analytics looking to strengthen their statistical toolkit with R.
- Students from Humanities with a basic mathematical and statistical background.
Course Prerequisites
To ensure you get the most out of this course, the following background is recommended:
- Mathematics knowledge up to Class 12 level.
- Some basic statistics background is desirable.
- It is highly preferred to complete the introductory courses:
- Introduction to R Software (NPTEL Link)
- Essentials of Data Science With R Software – 1: Probability and Statistical Inference (NPTEL Link)
Detailed 12-Week Course Layout
The course is structured to build your knowledge progressively, alternating between core statistical concepts and their practical application in R.
| Week | Topic |
|---|---|
| Week 1 | Introduction to data science and Calculations with R Software |
| Week 2 | Basic Fundamentals of Sampling |
| Week 3 | Simple Random Sampling |
| Week 4 | Simple Random Sampling with R |
| Week 5 | Stratified Random Sampling |
| Week 6 | Stratified Random Sampling with R |
| Week 7 | Bootstrap Methodology with R |
| Week 8 | Introduction to Linear Models and Regression and Simple linear regression Analysis |
| Week 9 | Simple Linear Regression Analysis with R |
| Week 10 | Multiple Linear Regression Analysis |
| Week 11 | Multiple Linear Regression Analysis with R |
| Week 12 | Variable Selection using LASSO Regression |
Key Learning Outcomes
By the end of this course, you will be able to:
- Understand the principles of sampling theory and design effective sampling strategies.
- Implement Simple Random Sampling and Stratified Random Sampling techniques using R.
- Apply the Bootstrap method for estimating sampling distributions and confidence intervals.
- Build, diagnose, and interpret Simple and Multiple Linear Regression models.
- Perform comprehensive regression analysis in R, from estimation to validation.
- Use advanced techniques like LASSO Regression for variable selection and handling multicollinearity.
- Interpret R output correctly to make data-driven decisions.
Recommended Textbooks & References
The course draws from a rich set of authoritative texts, including works by the instructor himself:
- Sampling Techniques by W.G. Cochran (Wiley)
- Sampling Methodologies and Applications by P.S.R.S. Rao (Chapman and Hall/CRC)
- An Introduction to the Bootstrap by Bradley Efron & R.J. Tibshirani
- Introduction to Linear Regression Analysis by Montgomery, Peck, & Vining (Wiley)
- Linear Models and Generalizations by C.R. Rao, H. Toutenburg, Shalabh, & C. Heumann (Springer, 2008)
- Introduction to Statistics and Data Analysis With Exercises, Solutions and Applications in R by Heumann, Schomaker, & Shalabh (Springer, 2016)
Industry Relevance
The skills taught in this course are not just academic; they are in high demand across the industry. All industries with an R&D or analytics setup, including IT, finance, healthcare, e-commerce, and manufacturing, require professionals who can perform robust statistical analysis. Mastery of sampling and regression with R makes you a valuable asset for roles in data analysis, business intelligence, and research.
Enroll in "Essentials of Data Science With R Software-2" to build an unshakable statistical foundation for your data science journey, guided by one of India's leading experts in the field.
Enroll Now →