This course is aimed at students with some previous experience in Python programming and a rudimentary knowledge of computational complexity. The goal is to provide students with a short introduction to many topics, so that they have an idea of what is feasible when the time comes later in their career to think about how to use computing to achieve some goal.
Students will spend a considerable amount of time writing programs to put into practice the concepts covered in the course.. Topics covered include tracing, stochastic programs, probability and statistics, random walks, Monte Carlo simulations, modeling data, optimization and clustering problems.
What will you learn?
If you successfully complete this course, will have:
- Developed an idea of the procedure of moving from an ambiguous problem statement to a computational formulation of a method for solving the problem,
- Learned a useful set of algorithmic and problem-minimizing techniques,
- You learned to use simulations to shed light on problems that do not easily succumb to closed-ended solutions.
- Learned to use computational tools, including simple statistical tools, machine learning and plotting, to model and understand data.
Duration
9 weeks
15 hours
Full time part time
Part time
