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

Lecture 07 - Confidence Intervals

Instructor: Prof. John Guttag. Prof. Guttag continues discussing Monte Carlo simulations.

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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