Introduction to Computational Thinking and Data Science
The direct continuation of MIT's 6.0001, this course keeps the same teaching team of Eric Grimson, John Guttag, and Ana Bell and moves from basic Python into computational approaches for analyzing data. Lecture videos, lecture slides, problem sets, and programming assignments cover optimization problems, stochastic thinking, random walks, Monte Carlo simulation, confidence intervals, and an introduction to machine learning through clustering and classification. It is cross-listed under both Computer Science and Probability and Statistics topics at MIT OpenCourseWare, reflecting how it blends programming with statistical reasoning. Finishing it leaves you able to build and evaluate simple predictive models in Python rather than just write procedural code.