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Statistics for Data Science II

This second course will develop on the first course on statistics and further delve into the main statistical problems and solution approaches

Course ID: BSCMA1004

Course Credits: 4

Course Type: Foundational

Recommended Pre-requisites: None

What you’ll learn

Recalling statistical modeling, description of data.
Applying Probability distributions and related concepts to the data sets
Explaining the concept of estimation of parameters.
Solving the problems related to point and interval estimation.
Explaining the concept of Testing of hypothesis related to mean and variance
Analysing the data using simple regression models and setting up relevant hypothesis tests

Course structure & Assessments

12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.

 WEEK 1 Multiple random variables - Two random variables, Multiple random variables and distributions WEEK 2 Multiple random variables - Independence, Functions of random variables - Visualization, functions of multiple random variables WEEK 3 Expectations Casino math, Expected value of a random variable, Scatter plots and spread, Variance and standard deviation, Covariance and correlation, Inequalities WEEK 4 Continuous random variables Discrete vs continuous, Weight data, Density functions, Expectations WEEK 5 Multiple continuous random variables - Height and weight data, Two continuous random variables, Averages of random variables - Colab illustration, Limit theorems, IPL data - histograms and approximate distributions, Jointly Gaussian random variables Probability models for data - Simple models, Models based on other distributions, Models with multiple random variables, dependency, Models for IPL powerplay, Models from data WEEK 6 Refresher week WEEK 7 Estimation and Inference I WEEK 8 Estimation and Inference II WEEK 9 Bayesian estimation WEEK 10 Hypothesis testing I WEEK 11 Hypothesis Testing II WEEK 12 Revision week

Prescribed Books

The following are the suggested books for the course:

Probability and Statistics with Examples using R. Author: Siva Athreya, Deepayan Sarkar and Steve Tanner

Andrew Thangaraj
Professor , Electrical Engineering Department , IIT Madras

Andrew Thangaraj received his B. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Madras in 1998 and Ph.D. in Electrical Engineering from the Georgia Institute of Technology, Atlanta, USA in 2003.

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He was a post-doctoral researcher at the GTL-CNRS Telecom lab at Georgia Tech Lorraine, Metz, France from Aug 2003 till May 2004. Since 2004, he has been a faculty at the Department of Electrical Engineering, IIT Madras, where he is currently a professor.

His research interests are in the broad areas of information theory, error-control coding and information-theoretic aspects of cryptography. From Jan 2012 till Jan 2018, he served as Editor for the IEEE Transactions on Communications. From July 2018, he is an Associate Editor for the IEEE Transactions on Information Theory.

From Nov 2011, he has been one of the NPTEL coordinators for IIT Madras. At NPTEL, he has played a key role in the starting of online courses and certification. He is currently a National MOOCs coordinator for NPTEL under the SWAYAM project of the MHRD.

Prof. Andrew is also one of the coordinators for the IIT Madras Online BSc Degree Program, which was launched in June, 2020.