Mar 7th 2017

6.00.2x: Introduction to Computational Thinking and Data Science (edX)

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This course is an introduction to using computation to understand real-world phenomena. This course 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:

- Plotting with the pylab package

- Random walks

- Probability, Distributions

- Monte Carlo simulations

- Curve fitting

- Knapsack problem, Graphs and graph optimization

- Machine learning basics, Clustering algorithms

- Statistical fallacies

What you'll learn:

- Plotting with the pylab package

- Stochastic programming and statistical thinking

- Monte Carlo simulations

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