Introduction to Computational Thinking and Data Science (MIT 6.0002)

MIT's 6.0002, the follow-on to its introductory Python course: 15 lectures of about 50 minutes each, roughly 12 hours. It uses Python to solve problems with optimisation and graph models, then turns to stochastic thinking, random walks and Monte Carlo simulation, confidence intervals, sampling and standard error, and understanding experimental data. The last lectures introduce machine learning with clustering and classification, and close with 'statistical sins', the common ways data is misused. A thoughtful bridge for students who know basic Python and want to think like data scientists.