Introduction to Computational Thinking and Data Science (Fall 2016) Prof. Eric Grimson , Prof. John Guttag and Dr. Ana Bell via MIT
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Introduction and Optimization Problems
Optimization Problems
Graph-theoretic Models
Stochastic Thinking
Random Walks
Monte Carlo Simulation
Confidence Intervals
Sampling and Standard Error
Understanding Experimental Data
Understanding Experimental Data (cont.)
Introduction to Machine Learning
Clustering
Classification
Classification and Statistical Sins
Statistical Sins and Wrap Up
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.
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