This course introduces bachelor’s and master’s students to data science methods for analysing physical measurements and drawing conclusions from noisy data. Topics include probability and statistics, frequentist and Bayesian inference, sampling methods, Markov chain Monte Carlo (MCMC), and an introduction to machine learning.
The course combines theoretical foundations with hands-on physics examples using Python and Jupyter notebooks. Students will explore datasets, fit models, quantify uncertainty, and critically evaluate their results.
Teaching consists of a weekly 90-minute lecture and a 90-minute tutorial every second week. The course is offered as a Studienleistung, requiring regular tutorial attendance and active engagement with the exercises and discussions.
- Trainer*in: Alessandro Ridolfi
- Trainer*in: Prajwal Voraganti Padmanabh