Introduction to Computational Thinking and Data Science

6.0002 is the second half of MIT's introductory programming sequence, following 6.0001, and the fall 2016 edition by Eric Grimson, John Guttag and Ana Bell is published on OpenCourseWare with all lecture videos. It is intended for students with little or no programming experience beyond the first course and aims to show how computation can be used to solve problems, using Python 3.5. The fourteen lectures open with optimisation problems and graph-theoretic models, then move into stochastic thinking, random walks, Monte Carlo simulation, confidence intervals, sampling and standard error. Two lectures on understanding experimental data lead into an introduction to machine learning, clustering, classification, and a closing lecture on classification and statistical sins.

The site provides the lecture slides and files, readings, the problem sets with programming assignments, and notes on the software used. The stated prerequisite is 6.0001 Introduction to Computer Science and Programming in Python or equivalent permission. Like everything on OpenCourseWare, the materials are free to use and download under a Creative Commons licence, with no registration and no certificate. MITx Online runs a graded counterpart, 6.00.2x, with an optional paid certificate.