6.0002
Introduction to Computational Thinking and Data Science
Fall 2016
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.
Readings
- SES #: 1 · TOPICS: Introduction and Optimization Problems · READINGS: Chapter 12.1, Chapter 5.4
- SES #: 2 · TOPICS: Optimization Problems · READINGS: Chapter 13
- SES #: 3 · TOPICS: Graph-theoretic Models · READINGS: Chapter 12.2
- SES #: 4 · TOPICS: Stochastic Thinking · READINGS: Chapter 14
- SES #: 5 · TOPICS: Random Walks · READINGS: Chapter 11, Chapter 14
- SES #: 6 · TOPICS: Monte Carlo Simulation · READINGS: Chapter 15.1–15.4, Chapter 16
- SES #: 7 · TOPICS: Confidence Intervals · READINGS: Chapter 16.4, Chapter 17
- SES #: 8 · TOPICS: Sampling and Standard Error · READINGS: Chapter 17
- SES #: 9 · TOPICS: Understanding Experimental Data · READINGS: Chapter 18
- SES #: 10 · TOPICS: Understanding Experimental Data (cont.) · READINGS: Chapter 18
- SES #: 11 · TOPICS: Introduction to Machine Learning · READINGS: Chapter 22
- SES #: 12 · TOPICS: Clustering · READINGS: Chapter 23
- SES #: 13 · TOPICS: Classification · READINGS: Chapter 24
- SES #: 14 · TOPICS: Classification and Statistical Sins · READINGS: Chapter 21
- SES #: 15 · TOPICS: Statistical Sins and Wrap Up · READINGS: Chapter 21
Sources
- Course catalogue ↗Historical OpenCourseWare
- MIT OCW readings ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.