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Data Science in Sports and Health (Seminar)
This page is currently under construction. Detailed course information will be added in the coming weeks. In the meantime, please refer to the university course catalog.
Course Information
Instructor: Alexander Krawczyk, Prof. Dr. Ralph Ewerth
Introduction session: April 16, 4 p.m.; tba
Block seminar: 2 days in June/July 2026 (presence mandatory)
Target Audience
Bachelor/Master students in Computer Science, and Data Science (maximum 14 participants)
Prerequisites: Students are strongly encouraged to have completed or be enrolled in an introductory course in machine learning and/or computer vision.
Course Overview
Data Science and Machine Learning are transforming the analysis of multimodal data in sports and training sciences. Applications range from machine learning–based computer vision techniques that extract semantic insights from images and videos, to large vision-language models (LVLMs) capable of interpreting data across multiple domains and modalities. These methods enable informed prediction, improved in-game decision-making, and comprehensive post-hoc performance analysis in sports and health contexts.
The Master’s seminar “Data Science in Sports and Health” offers students the opportunity to explore and discuss the latest trends and state-of-the-art methods for solving complex analytical problems. Topics include applied computer vision algorithms, advanced statistical modeling, and deep learning approaches for domains such as game state reconstruction, training optimization, injury risk prediction, and real-time performance monitoring.
Students will engage deeply with current research advances in three main areas:
- Applied Computer Vision for Sports Analytics
- Machine Learning and Data Science in Sports and Training Sciences
- Bias, Fairness, and Ethical Aspects of Data-Driven Sports Analysis
Qualification:
The seminar is designed to strengthen essential academic and research skills. Students will learn to critically synthesize scientific literature, assess the strengths and limitations of modern data science methods, identify current research gaps, and articulate the potential impact of emerging technologies in sports and training science. Through presentations, discussions, and written reports, participants will enhance their ability to communicate advanced concepts to interdisciplinary audiences and conduct independent research work.
Format:
This research-oriented seminar is based on active student participation. Each participant will present a synthesis of recent research papers on an assigned topic and lead the subsequent group discussion. In addition, students will submit a final term paper offering an in-depth exploration of a chosen topic.