Bridging Theory and Practice: A Matrix-Based Approach to Teaching Medical Data Science with MIMIC-IV Demo Dataset

Medical data science education often separates theoretical instruction from practical application, resulting in fragmented learning experiences that fall short of preparing students for real-world data analysis challenges. To better familiarize students with such challenges, we have developed and evaluated an integrated 8 European Credit Transfer and Accumulation System (ECTS) course for master's students in computer science that combines lectures, seminars, and exercises focusing on practical key challenges of health data processing. The course employs a matrix structure (15 intensive care diseases × 4 informatics foci) based on disease-specific projects using the freely available MIMIC-IV Demo dataset. Particular emphasis was placed on teaching technical skills that are essential for processing raw data, including across different industries: groups of students developed complete (E)xtract, (T)ransform, (L)oad (ETL) pipelines in accordance with the medallion architecture (bronze-silver-gold levels) and, in parallel, conducted structured reviews in accordance with PRISMA guidelines. 87 % of enrolled students qualified for examinations. The examination pass rate was 91 %. Students particularly value authentic data challenges and transferable data processing frameworks, such as the medallion architecture. This integrated design successfully bridges theory and practice in medical data science education, providing transferable skills through real-world data and systematic methodology. The freely available dataset enables reproducibility by other institutions.