Introduction to Computational Thinking and Data Science

Source: https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/

Lecture 1: Introduction, Optimization Problems

Lecture 2: Optimization problems

Lecture 3: Graph-theoretic models

Lecture 4: Stochastic Thinking

Lecture 5: Random Walks

Lecture 6: Monte Carlo Simulation

Lecture 7: Confidence Intervals

Lecture 8: Sampling and standard error

Lecture 9: Understanding Experimental Data

Lecture 10: Understanding Experimental Data (Cont.)

Lecture 11: Introduction to Machine Learning

Lecture 12: Clustering

(Pending)

Lecture 13: Classification

(Pending)

Lecture 14: Classification and statistical sins

(Pending)

Lecture 15: Statistical Sins and Wrap Up

(Pending)