Introduction to Computational Thinking and Data Science
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University :
Massachusetts Institute of Technology
Instructors :
John Guttag
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Course Goals
6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . 
This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. 
You will spend a considerable amount of time writing programs to implement the concepts covered in the course. 
For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient's body.

Topics covered include:
(1) Advanced programming in Python 3
(2) Knapsack problem, Graphs and graph optimization
(3) Dynamic programming
(4) Plotting with the pylab package
(5) Random walks
(6) Probability, Distributions
(7) Monte Carlo simulations
(8) Curve fitting
(9) Statistical fallacies

Course Syllebus
(1) Plotting with the pylab package
(2) Stochastic programming and statistical thinking
(3) Monte Carlo simulations
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