This page lists all courseware - lecture topics, slides, and reading material - for external access. Students of the course at IIT Madras should use the Google Classroom for accessing material, lecture videos, discussion forums, problem sets, and grades. The Julia notebooks used in the course are available to be tried out online through Binder at this GitHub repository.

Please drop me an email (firstname @ cse.iitm.ac.in) in case you find a missing or broken link.

Pre-requisites

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• A first course on Probability (eg. CS6015 or EE5110 or MA5400)

• A first course on Programming in C/C++ or Python or Julia

A bit about the course

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• This is intended as a 'statistical' approach to doing data science, i.e., learn enough of statistics to apply to analysis of real-world data.
• We use Julia a cool and potentially important programming language throughout the course. (There is a gentle introduction to it).
• A significant fraction of the lectures demonstrate ideas interactively on Julia with Pluto notebooks. These notebooks are included in the material below.

Courseware

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Topic	Lecture material	Comments
Statistics - An Overview	Lecture Slides	An opinionated view of the key ideas in statistics and mapping of these ideas to the modules of the course
Julia - An introduction	Pluto notebook	A very quick introduction to Julia
Probability Refresher on Julia	Pluto notebook	Illustration of some common ideas from probability - Random numbers, sampling, distributions, density functions, independence, and Monte Carlo simulations
Assignment 1: Probability	Question sheet	Usual probability questions with cards and random events, but with a computational focus.
History of Statistics	Lecture Slides	A 90-slide deck tracing the history of ideas in statistics for three millennia.
Dataframes	Pluto notebook	An introduction to storage of relational data, and some important operations on them: joins, split-apply combine, and reshape