6.0002 Introduction to Computational Thinking and Data Science (Fall 2016, MIT OCW) | Computer Science

6.0002 Introduction to Computational Thinking and Data Science

6.0002 Introduction to Computational Thinking and Data Science (Fall 2016, MIT OCW). Instructors: Prof. Eric Grimson and Prof. John Guttag. 6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python 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. (from ocw.mit.edu)

Introduction

Lecture 01 - Introduction and Optimization Problems

Lecture 02 - Optimization Problems

Lecture 03 - Graph-theoretic Models

Lecture 04 - Stochastic Thinking

Lecture 05 - Random Walks

Lecture 06 - Monte Carlo Simulation

Lecture 07 - Confidence Intervals

Lecture 08 - Sampling and Standard Error

Lecture 09 - Understanding Experimental Data

Lecture 10 - Understanding Experimental Data (cont.)

Lecture 11 - Introduction to Machine Learning

Lecture 12 - Clustering

Lecture 13 - Classification

Lecture 14 - Classification and Statistical Sins

Lecture 15 - Statistical Sins and Wrap Up

References

6.0002 Introduction to Computational Thinking and Data Science (Fall 2016)
Instructors: Prof. Eric Grimson and Prof. John Guttag. Lecture Notes. Assignments: Problem Sets (no solutions). Assignments: Programming (no examples). It aims to provide students with an understanding of the role computation can play in solving problems.