Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI
 

Data Management for Digital Health (Wintersemester 2019/2020)

Dozent: Dr.-Ing. Matthieu-P. Schapranow (Digital Health - Personalized Medicine)
Tutoren: M.Sc. Florian Borchert
Website zum Kurs: https://hpi.de/digital-health-cluster/teaching/archive/winter-term-201920/data-management-for-digital-health.html

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 01.10.-30.10.2019
  • Lehrform: Vorlesung / Übung
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-K Konzepte und Methoden
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-T Techniken und Werkzeuge
  • BPET: Business Process & Enterprise Technologies
    • HPI-BPET-S Spezialisierung
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-K Konzepte und Methoden
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-S Spezialisierung
Data Engineering MA
Digital Health MA

Beschreibung

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.

More about the course

Leistungserfassung

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

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

  • Final exam

Termine

Tuesdays, 11 am & Thursdays, 9 am  in G3 E. 15/16

Lecture starts October 14

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