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Foundational Level Course
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 .
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.
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
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
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