Catalog

Introduction to Computational Thinking and Data Science

Electrical Engineering and Computer Science MIT CC BY-NC-SA 4.0 15 lectures

6.0002 is the continuation of _[6.0001 Introduction to Computer Science and Programming in Python](/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/)_ and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

Syllabus

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

Course materials