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Module Number:
| 14102
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| Module Title: | Biomedical Data Science |
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Biomedizinische Datenwissenschaften
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| Department: |
Faculty 2 - Environment and Natural Sciences
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| Responsible Staff Member: | -
Prof. PD Dr. rer. nat. habil. Rödiger, Stefan
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every winter semester
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| Credits: |
6
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| Learning Outcome: | The studies aim to provide an interdisciplinary education, combining medicine, computer science, and data analysis skills. Students learn to work with small exemplary real world datasets from biomedical research. The goal is to enable graduates to derive new knowledge, more accurate diagnoses, and improved therapies by connecting different areas of expertise. Upon completing the Bachelor course, students will be able to:
- Apply statistical concepts and methods to analyse and interpret biomedical data, including hypothesis testing, regression, and machine learning.
- Use bioinformatic tools and databases to analyse and interpret genomic, proteomic, and other types of biomedical data.
- Program in R or Python to perform data analysis, visualization, and modelling tasks, including data wrangling, data visualization, and statistical modeling.
- Use markup languages (YAML, Markdown, ECMAScript) to document and communicate biomedical data analysis results.
- Apply data exchange formats (JSON, XML) to import, export, and manipulate biomedical data.
- Use LaTeX to create professional-quality documents and reports for biomedical data analysis results.
- Integrate multiple data sources and formats to perform comprehensive biomedical data analysis.
- Evaluate the quality and limitations of biomedical data and develop strategies for data validation and quality control.
- Communicate complex biomedical data analysis results effectively to diverse audiences, including healthcare professionals, researchers, and policymakers.
- Learn about ethical and legal principles to biomedical data analysis, including issues related to data privacy, security, and informed consent.
Overall, biomedical data science programs provide a broad foundation in medicine, informatics, and data analysis methods, with opportunities to deepen knowledge. |
| Contents: | Part 1: Introduction to Biomedical Data Science
Part 2: Programming for Biomedical Data Analysis
Part 3: Bioinformatic Analysis of Biomedical Data
Part 4: Data Exchange Formats and Markup Languages
Part 5: Data Visualization and Communication
Part 6: Biomedical Data Integration and Validation
Part 7: Special Topics in Biomedical Data Science
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| Recommended Prerequisites: | none |
| Mandatory Prerequisites: | none |
| Forms of Teaching and Proportion: | -
Lecture
/ 4 Hours per Week per Semester
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Self organised studies
/ 120 Hours
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| Teaching Materials and Literature: | - "Introduction to Biomedical Data Science" by Robert Hoyt and Robert Muenchen
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| Module Examination: | Final Module Examination (MAP) |
| Assessment Mode for Module Examination: | Written examination (120 min) |
| Evaluation of Module Examination: | Performance Verification – graded |
| Limited Number of Participants: | None |
| Part of the Study Programme: | -
Bachelor (research-oriented) /
Life Science and International Health /
PO 2025
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| Remarks: | online lecture |
| Module Components: | - Lecture / 4 Hours per Week per Semester
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| Components to be offered in the Current Semester: | |
