The complexity, scale, and messiness of biological data require computational tools, but also critical thinking and an understanding of computational science. This course provides a solid but accessible introduction to the mathematical, statistical, and computational foundations needed to handle incomplete data, construct models, and draw meaningful conclusions. Designed as a “bootcamp” for students without a background in computational science, it focuses on developing skills in the languages of data analysis, modeling, and algorithmic thinking. Students learn to quantify variation, adapt models, and structure real-world systems as networks, equations, or code. The course aims to lay the foundation for independent and critical engagement in modern quantitative biology and biotechnology.
The course is based on an interactive and practically oriented pedagogical method. Each lecture is accompanied by a guided computer exercise in Python. The approach reinforces practical skills in parallel with theoretical understanding. Students gradually build up their own reference material and carry out a project where they apply the course concepts to a computational problem in the life sciences.