Research and 

Statistics 2 :

Course Overview: This course, guided by Dr. Andrew Thangaraj, Professor in the Electrical Engineering Department at IIT Madras, provides an in-depth exploration of probability and statistics. It is designed to equip learners with the foundational knowledge and practical skills necessary to apply statistical methods and probabilistic reasoning across various fields.

Course Faculty and Instructors:

• Faculty:

  • Dr. Andrew Thangaraj, Professor, Electrical Engineering Department, IIT Madras

• Course Instructors:

  • Nikita Kumari (M.Sc., Mathematics, IIT Madras)

  • Mayur Gundal (M.Tech., Ocean Engineering, IIT Madras)

  • Prashant Sharma (M.Sc., Statistics, University of Delhi)

• Course TAs:

  • Ankit Kumar

  • Kshitij Pandey

Study Material: The primary resources for this course are the video lectures and assignments available on the course page. These materials are designed to provide comprehensive coverage of the topics discussed in the lectures and to reinforce learning through practical application.

Prescribed Textbooks:

1. Probability and Statistics with Examples using R by Siva Athreya, Deepayan Sarkar, and Steve Tanner

2. Probability & Statistics for Engineers & Scientists (Global Edition) by Ronald E. Walpole

These textbooks serve as supplementary resources to deepen your understanding of the concepts covered in the lectures and assignments.

Interaction Sessions: Learners are encouraged to utilize the interaction sessions with course support members to clarify any doubts and enhance their understanding of the subject matter. These sessions provide an excellent opportunity to engage with the instructors and TAs for personalized guidance.

Course Syllabus and Calendar: For detailed information about the course syllabus, instructors, and prescribed books, and to view the course-specific calendar, please visit the course page using the provided link: IIT Madras Online Degree Course Page

Key Topics Covered:

• Fundamentals of probability

• Descriptive statistics

• Inferential statistics

• Probability distributions

• Statistical inference

• Hypothesis testing

• Regression analysis

• Analysis of variance (ANOVA)

• Applications of statistics in engineering and science

Learning Outcomes: By the end of this course, learners will be able to:

• Understand and apply basic principles of probability and statistics.

• Analyze data using statistical methods.

• Use R programming for statistical analysis.

• Interpret and communicate statistical results effectively.

• Apply statistical reasoning to solve real-world problems in engineering and science.

This course is tailored for learners who aim to build a strong foundation in probability and statistics, enhancing their analytical skills and preparing them for advanced studies or professional applications in various domains.