Biomedizinische Datenwissenschaften

• 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.

Written examination (120 min)

"Introduction to Biomedical Data Science" by Robert Hoyt and Robert Muenchen

on campus, optional online

Bewertungen von Studierenden

Was Studierende über dieses Modul sagen. Die BTU hat damit nichts zu tun — hier schreiben nur Studierende, und jede Bewertung ist die Meinung einer einzelnen Person.