RealWorld Data Analysis
Module Description
Real World Data Analysis (RWDA)
6 ECTS
Objective
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.
Grading
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.
Requirements
- 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.
Place
The lectures of the course take place at the Klinikum Großhadern in the department of
Anaesthesiology (cube FG, 2nd floor).
Contact
Andrea Becker-Pennrich, andrea.beckerpennrich@med.uni-muenchen.de