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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
8. Sampling and Standard Error
9. Understanding Experimental Data
15. Statistical Sins and Wrap Up
2. Optimization Problems
4. Stochastic Thinking
6. Monte Carlo Simulation
14. Classification and Statistical Sins
3. Graph-theoretic Models
7. Confidence Intervals
11. Introduction to Machine Learning
13. Classification
5. Random Walks
10. Understanding Experimental Data (cont.)
12. Clustering
1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)
