Course contents: Topological data analysis has in recent years become an important tool in data science. This course explores applications of topological data analysis in the natural sciences. A prominent example will be critical mutations in the evolution of viruses. We will learn about methods (persistent homology and the Mapper algorithm), study the underlying mathematical theory, and design and implement computer algorithms in concrete examples.

Intended audience: Everyone with a background in mathematics/computer science/natural sciences.

Prerequisites: Basic linear algebra and calculus, basic algebraic topology (as taught in the course "Topological Data Analysis" in WS 2020/21, available on ILIAS), basic computer programming skills (Python).