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.
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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
This course is part of theComputational Thinking using Python XSeries program.
What you'll learn:
- Plotting with the pylab package
- Stochastic programming and statistical thinking
- Monte Carlo simulations
Prerequisites
6.00.1x - Introduction to Computer Science and Programming Using Python or equivalent (some prior programming experience in Python and a rudimentary knowledge of computational complexity)
