The DZD Health Data Scientist & AI Certificate Program (DZD HeaDS) is a two-year advanced training program coordinated by the German Center for Diabetes Research (DZD).
The program is designed to attract STEM Master students and early-career doctoral researchers to data-driven health research and to integrate them into an interdisciplinary network of experts across health research, with a particular focus on diabetes and metabolism.
DZD HeaDS combines structured training with hands-on experience, enabling participants to develop practical skills in health data collection, standardization, analysis, and innovation. Through collaborative activities and close interaction with the DZD community, participants gain insight into real-world research challenges and build a strong professional network.
Health Data Science in the DZD, get to know participants and buddies. See detailed information.
Understanding the medical context behind the data: Introduction to translational health research. How does medical research work? How are insights generated? What is translational research? The Translational Ecosystem with the DZD as example
From organs to physiological parameters: Diabetes – a multiorgan disease, Imaging methods for diagnosis and research; Basic anatomy and physiology of organs (in diabetes pathology) and associated health data (physiological parameters typically assessed in (clinical) studies and clinical routines).
Understanding diabetes: basic concepts, pathogenesis, diagnosis and therapy. Towards a precision medicine – concept, research, status quo.
Clinical study design: best practice of data management and statistics for sustainable, high quality data.
Overview of different medical data sources, ensuring good data quality, health data infrastructures in Germany and the EU, ethical consideration and regulatory aspects of health data.
Visit to the DZD clinical study center in Tübingen.
Essential for participants with little or no prior knowledge in computer science, recommended for all. The course refreshes knowledge in machine learning and deep learning. Introduction to standards of modern programming style (GitHub, version control, open source etc.).
Health data infrastructures, FAIR principles, the role of core data sets in health data science, methods of data integration and knowledge management.
Big data analytics, methods for visualization of health, application of machine learning in python on real world health data.
Current technological approaches in diabetes treatment and epidemiological models to improve prevention, diagnosis and treatment of metabolic diseases. From integration of multiomics data to innovations of health research; principles of entrepreneurship.
Real data – real problems: the most innovative solution wins!
Meet with experts from all fields related to health data science.
Optional individual short research stays in DZD research groups to work on a scientific project.