Introduction to Computational Thinking and Data Science

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: Advanced programming in Python 3 Knapsack problem, Graphs and graph optimization Dynamic programming Plotting with the pylab package Random walks Probability, Distributions Monte Carlo simulations Curve fitting Statistical fallacies

• Plotting with the pylab package
• Stochastic programming and statistical thinking
• Monte Carlo simulations

• 6.00.1x or equivalent (some prior programming experience in Python and a rudimentary knowledge of computational complexity)