HPI Digital Health Cluster

Data Management for Digital Health

General Information

  • You are viewing an archived version of the course, please find a newer version here
  • Teaching staff: Benjamin Bergner, Florian Borchert, Harry Freitas Da Cruz, Orhan Konak, Dr.-Ing. Matthieu-P. Schapranow
  • Format: 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Location: HPI Campus III, G3-E.15/16
  • Schedule:
    • Tuesdays 11.00am (s.t.)
    • Thursdays 09.15am (s.t.) 
  • First course:
    • Tuesday Oct 15, 2019 at 11.00am on HPI Campus III in room G3-E.15/16


  • Tue Oct 29, 2019: Faculty announced dies academicus for this date
  • Thu Oct 31, 2019: Reformation Day, which is bank holiday in Brandenburg 

Scope of the lecture

Welcome to the class: we are very excited that you are interested in learning more about the foundations data management for digital health. In this lecture, we will provide you concrete examples from the field of digital health to understand where and how data is acquired, what are the challenges with these specific types of data, and how to handle them with latest technology advances.

After participating in the course, you will be equipped with the ability to: 

  • assess requirements of selected real-world use cases from the medical field,
  • select latest technology building blocks to create viable healthcare software solutions, and
  • analyze requirements for data analysis and processing, e.g. for machine learning.

In the course, we will have invited guest speakers sharing their real-world experience with you in a brief presentation. You will also have the chance to raise your questions and discuss with them in the course of the lecture. Furthermore, you will have the chance to gather hands-on experiences, e.g. you will have the chance to join on-site visits of our cooperation partners in Berlin.

Further details about the structure of the lecture will be shared in the first course with you.


The final grading will be determined by the following individual parts, while each part must be passed individually: 

  • Intermediate exercises during the lecture whilst all of them need to be passed
  • Final exam