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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
About this film
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag This course provides students with an understanding of the role computation can play in solving problems. Student will learn to write small programs using the Python 3.5 programming language. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Directors & creators
MIT OpenCourseWare
Subjects
Python 3.5, Python, machine learning, knapsack problem, greedy algorithm, optimization, weights, models, computational thinking, data science, dynamic programming, recursion, exponential time, stochastic, random, probability, independent variables, dependent variables, monte carlo simulation, simulation, population sampling, law of large numbers, variance, confidence interval, empirical rule, standard deviation, central limit theorem, bias, error distribution, sampling, error bars, numpy, scipy, matplotlib, pylab, python, plotting, graphing, supervised learning, computer modelling, signal-to-noise, feature vectors, classification model, regression model, classification, classifier, nearest neighbors, feature scaling, decision trees, entropy, trainig data, clustering, cluster analysis, unsupervised learning, objective function, dendogram, statistical fallacy, systematic errors, correlation and causation, misleading statistics, GIGO, axis truncating, extrapolation, data enhancement, Texas Sharpshooter Falacy
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