This course provides a comprehensive journey from statistical modeling to advanced data analysis techniques, incorporating elements of modern research workflows in particle physics. Students will learn core topics such as probability distributions, parameter estimation, hypothesis testing, uncertainty analysis, multivariate techniques, and machine learning for particle classification, alongside model fitting and evaluation. Weekly lectures will be followed by hands-on application to a freshly collected dataset from the LHCb experiment in 2024. Students will progressively build their skills by analyzing real research data, fitting theoretical models, and interpreting uncertainties. The course also introduces Julia programming to equip students with modern computational tools. Bi-weekly tutorials will serve as hackathon-style workshops, providing direct support for advancing coding projects and preparing for upcoming tasks. By the end, students will complete an independent data analysis project and present their findings, demonstrating their proficiency in high-energy physics data analysis.
- Kursleiter/in: Mikhail Mikhasenko