Bio

Andrew Thangaraj received his B.Tech in Electrical Engineering from the Indian Institute of Technology (IIT), Madras, India in 1998 and a PhD in Electrical Engineering from the Georgia Institute of Technology, Atlanta, USA in 2003. He was a post-doctoral researcher at the GTL-CNRS Telecom lab at Georgia Tech Lorraine, Metz, France from August 2003 to May 2004. From June 2004, he has been with the Department of Electrical Engineering, IIT Madras, where he is currently a professor. From Jan 2012 to Jan 2018, he served as Editor for the IEEE Transactions on Communications. From July 2018 to July 2022, he served as an Associate Editor for Coding Techniques for the IEEE Transactions on Information Theory.

Since Oct 2011, he has been serving as NPTEL coordinator at IIT Madras. He has played a key role in initiating and running NPTEL online courses and certification. He is currently the PI of the SWAYAM project of the Ministry of Education, Government of India.

Since May 2020, he has been serving as Coordinator for the BS (Data Science) Program at IIT Madras. Since May 2024, he has been serving as Chair for the Centre for Outreach and Digital Education (CODE) at IIT Madras.

For more details on the philosophy and impact of NPTEL and IITM's BS (Data Science) program, see the keynote talk below in a G20 Education Summit held at IIT Madras Research Park in Jan 2023.

About Course:

The Statistics-2 course in the IIT Madras BS Degree program in Data Science typically covers intermediate concepts in statistics, focusing on both theoretical understanding and practical application. Here is a summary of the key topics you might expect:

1. Estimation Theory:

   • Point Estimation: Properties such as unbiasedness, consistency, and efficiency.

   • Interval Estimation: Constructing confidence intervals for population parameters.

   • Methods of Estimation: Maximum likelihood estimation (MLE), Method of Moments.

2. Hypothesis Testing:

   • Null and Alternative Hypothesis: Concept of testing statistical hypotheses.

   • Type I and Type II Errors, Power of a Test.

   • Tests for Population Mean, Variance, and Proportions: Z-test, t-test, chi-square test, F-test.

   • p-value interpretation.

3. Analysis of Variance (ANOVA):

   • One-way and Two-way ANOVA.

   • Application in comparing multiple means to identify significant differences.

4. Regression Analysis:

   • Simple Linear Regression: Understanding relationships between two variables.

   • Multiple Linear Regression: Examining the influence of multiple factors.

   • Estimation of Regression Coefficients, Residual Analysis.

5. Non-Parametric Methods:

   • Introduction to techniques for data that violates normality assumptions.

   • Key Tests: Mann-Whitney U Test, Kruskal-Wallis Test.

6. Goodness of Fit and Contingency Tables:

   • Chi-Square Goodness of Fit Test.

   • Tests for Independence in contingency tables.

7. Statistical Software:

   • Hands-on application using tools like R or Python for data analysis.

The course involves practical assignments to reinforce these concepts, helping students understand both the theory and it's real-world applications.