Data Governance and Engineering

A major challenge for future epidemiologists is that healthcare data is often fragmented, originating from diverse sources such as electronic health records (EHRs), wearables, and diagnostic equipment. These data are typically not collected with research purposes or specific research questions in mind. Additionally, ethical and legal challenges - such as ensuring privacy, obtaining informed consent, upholding fairness, and complying with data usage regulations - add further complexity. This course explores the role of data governance and engineering in managing these challenges. You will develop an understanding of the systems, technologies, and processes involved in collecting, storing, and preparing data for analysis. They will also learn about the diversity of data sources and formats, the importance of robust data governance frameworks (including regulatory requirements like GDPR/AVG), and essential data wrangling techniques to transform raw data into usable formats for research.

After completion of the course, you should be able to:

• describe the principles of data management, governance, and infrastructure (including data quality, interoperability, privacy, and regulatory compliance, e.g., GDPR);
• implement data governance practices (including data accuracy, accessibility, security, and ethical/regulatory compliance);
• describe the diversity of data sources and types in health data science (structured data, text, images, molecular data).;
• explain the data sources’ relevance to research and clinical applications.
• perform data processing (and wrangling) to prepare datasets for analysis

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

1 week, fulltime

Please note that this course is part of an existing program within the Graduate School of Life Sciences. Tuitition fees may alter during the year.