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Introduction to Computational Thinking and Data Science

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Introduction to Computational Thinking and Data Science
6.00.2x is an introduction to using computation to understand real-world phenomena.
MIT Online Course Highlights
  • 9 weeks long
  • 14-16 hours per week
  • Learn for FREE, Upgradable
  • Self-Paced
  • Taught by: John Guttag, Eric Grimson, Ana Bell
  • View Course Syllabus

Online Course Details:

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

What You Will Learn

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

Take This Online Course