Python is a leading programming language for scientific research, data science, and machine learning. This course will familiarize students with the Python scientific stack and with best practices for scientific computing using methods from dynamical systems, stochastic processes, classical statistics, numerical analysis, Bayesian statistics, and artificial neural networks.
Every class will present a scientific problem, a computational method for tackling it, and a Python implementation of the method. Examples will include predicting points in tennis matches and survival on the Titanic, modelling evolutionary dynamics and infectious diseases, finding stationary points for a predator-prey system, inference in animal social networks, and classification of handwritten digits.
Instructor: Yoav Ram
Language: The course will be taught in English.
Environment: The course will be given using interactive Jupyter notebooks with built-in exercises and problems. Students can work in the cloud (Azure Notebooks) or on their own computer (Anaconda).
Prerequisites: Python, NumPy & Matplotlib, Calculus, Linear Algebra, Probability. See more details below.