Data Governance and Engineering

Course goals

ALL PARTICIPANTS WILL BE PLACED ON THE WAITING LIST UNTIL TWO WEEKS BEFORE THE START OF THE COURSE

GRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)

POSTGRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)  or online (depending on your registration)

(FYI: the other options are options for Continuing Education (onderwijs voor professionals))

After completion of the course, the student is 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

Content

Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl

Registration:
Students can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site. NOTE Students of the MSc Epidemiology (Post Graduate) that register in time (i.e. at least two weeks before the start of a course) will always be admitted to the course unless it is completely full. Other students will receive information about their application two weeks before the start of the course.

Course coordinator:
Dr. C.L. (Constanza)  Andaur Navarro, UMC Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, the Netherlands
 
Course description:
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. Students 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.

Literature/study material used:
NA
  
Mandatory for students in own Master’s programme:
No
 
Optional for students in other GSLS Master’s programme:
Yes
 
Prerequisite knowledge:
Introduction to Epidemiology
Introduction to Statistics
Study Design in Etiological Research
Classical Methods in Data Analysis
Modern Methods in Data Analysis