MIT 6.0002 — Introduction to Computational Thinking and Data Science MIT OpenCourseWare | Fall 2016 | Python Course Page Progress Week 1 — Optimization Problems Week 2 — Graph Models / Stochastic Thinking Week 3 — Random Walks / Monte Carlo Simulation Week 4 — Confidence Intervals / Sampling Week 5 — Understanding Experimental Data Week 6 — Introduction to Machine Learning / Clustering Week 7 — Classification / Statistical Sins Weekly Notes & Code Week 1 # Lecture Notes Code 01 Introduction and Optimization Problems Notes Code 02 Optimization Problems Notes Code Problem Set: PSet-01 Week 2 # Lecture Notes Code 03 Graph-theoretic Models Notes Code 04 Stochastic Thinking Notes Code Problem Set: PSet-02 Week 3 # Lecture Notes Code 05 Random Walks Notes Code 06 Monte Carlo Simulation Notes Code Problem Set: PSet-03 Week 4 # Lecture Notes Code 07 Confidence Intervals Notes Code 08 Sampling and Standard Error Notes — Problem Set: PSet-04 Week 5 # Lecture Notes Code 09 Understanding Experimental Data Notes Code 10 Understanding Experimental Data (cont.) Notes Code Problem Set: PSet-05 Week 6 # Lecture Notes Code 11 Introduction to Machine Learning Notes Code 12 Clustering Notes Code Week 7 # Lecture Notes Code 13 Classification Notes Code 14 Classification and Statistical Sins Notes Code 15 Statistical Sins and Wrap Up Notes Code