Python is one of the leading programming languages for scientific research, data science, and machine learning. The course will familiarize students with the Python scientific stack, including NumPy, SciPy, Matplotlib, and Pandas, and best practices for scientific computing. Every class will present a scientific problem, a method for solving it, and an implementation in Python. Examples will include how to model bacterial growth, find stationary points for a predator-prey equation system, predict results of a tennis game, and plot a map of hurricane density.
Students will present papers which analyse computational models that arise in biology. Models will include discrete and continuous-time dynamics, in deterministic and stochastic settings, with applications to molecular biology, population dynamics, and population genetics.