An introduction to modelling in ecology, evolution, and epidemiology and to scientific computing with the Python programming language for biology graduate students
The course will introduce graduate students to models in population biology. We will build models, analyze them using mathematical and computational methods, and fit them to empirical data using statistical methods such as maximum likelihood and Bayesian inference.
Every class will present a scientific problem in population biology, a computational method for tackling it, and a Python implementation of the method. Examples will include models from ecology, evolution, epidemiology, and social behavior. In each case, we will introduce a research question, design a model, choose a method, apply it using the Python program language (a leading programming language for scientific research, data science, and machine learning), analyze and visualize the results, and discuss the conclusions.
- A Biologist's Guide to Mathematical Modeling in Ecology and Evolution / Sarah P. Otto and Troy Day
- IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant
Content of this repository is distributed under the CC-BY-SA 4.0 license.