RealWorld Data Analysis

Module Description
Real World Data Analysis (RWDA)

Students learn to extract and analyze data from a public intensive care unit database (MIMIC-IV) based on a provided or self-defined research question. The goal of the course is to face as well as overcome challenges arising from working with real world data.

The grading is based on two presentations (=progress reports, at the end of the first and second week, each 30%) and a final data analysis report by the end of August (40%). The final data analysis report will be in the style of a research paper (word count: 3000) that could potentially be submitted to a journal.

We will grade the students’ approach to the research question, the final report as well as style and quality of each presentation. The result of the analysis will not be a part of the grade. The whole group will receive the same grade.

Description and Structure
The course structure is more seminar-like. There will be lectures at the beginning of the first week, afterwards students can work on a research question in groups of 2-4 on their own schedule. (Online) support is provided during business hours. We aim to use Python to extract as well process the data. A basic script/tutorial for learning Python (~1h of reading, ~1-2h of practicing) to accomplish these tasks will be provided beforehand.

Target audience

  • Students that have attended the Medical Informatics lectures as part of „Clinical Epidemiology“ or have a basic understanding of current data science technologies.
  • Students that are willing to learn the basics of data extraction with the Python programming language.
  • Experience with (modern) programming or data extraction languages is recommended.


  • Willingness to learn the basics of data extraction with the Python programming language.
  • Willingness to work in a team on presentations and a research paper/manuscript.
  • Self-organization and good time management.

The lectures of the course take place at the Klinikum Großhadern in the department of
Anaesthesiology (cube FG, 2nd floor).

Andrea Becker-Pennrich, andrea.beckerpennrich@med.uni-muenchen.de