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CS 544 — Data Science in Biomedicine
(dt. Data Science in Biomedizin)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (2 SWS), recitation class (2 SWS),
180 hours (60 h attendance, 120 h private study)
Credit points,
formal requirements		 6 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Oral examination (individual examination) or written examination
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science.
Duration,
frequency		 One semester,
Regelmäßig alle 2 Semester
Person in charge of the module's outline Prof. Dr. Dominik Heider

Contents

Selected topics from the field of Data Science for application in biomedicine, especially from the field of biological data processing (e.g. alignments, coding), as well as methods from the fields of statistics (e.g. statistical tests, evaluation) and sta-tic learning (e.g. random forests). The methods will be presented in the lecture. In the exercise, their application is practiced on concrete case studies.


Qualification Goals

Students will be familiar with the most important methods from the field of biomedical data science that are required for calculations in the natural sciences. They have understood these methods and are able to select, perform and implement suitable methods for specific case studies.


Prerequisites

None. The competences taught in the following modules are recommended: Machine Learning and Introduction to Statistics or Elementary Probability and Statistics or Elementary Stochastics.


Applicability

Module imported from M.Sc. Data Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Business Informatics

When studying B.Sc. Data Science, this module can be attended in the study area Free Compulsory Elective Modules.

The module is assigned to Computer Science. Further information on eligibility can be found in the description of the study area.


Recommended Reading

  • Witten, Frank und Hall: Data Mining, Morgan Kaufmann
  • Weitere Literatur wird in der Lehrveranstaltung bekanntgegeben.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2023/24. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

  • Winter 2016/17 (no corresponding element)
  • Summer 2018 (no corresponding element)
  • Winter 2018/19 (no corresponding element)
  • Winter 2019/20 (no corresponding element)
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.