Data Science using R (DSR)
1/2. Semester
5 ECTS | 4 SWS
Written Exam 90 Minutes (K90)
Analyze Data with R: Develop scripts and notebooks in R, apply statistical methods and machine learning techniques to biomedical and business datasets, and validate hypotheses using data-driven analyses.
Contents
- Introduction into R (e.g. RStudio, Visual Studio Code or Positron)
- Advanced concepts of R programming (functional programming, unit tests, packages, integration of other languages)
- Graphics and Visualizations
- Data integration (CSV files and data base access etc.)
- R Markdown and Notebooks
- Distributions, Estimators, Confidence Intervals and Tests in R
- Unsupervised statistical learning with R packages (Clustering, Principal Component Analysis)
- Supervised statistical learning with R packages (Lin. Regression, Lin. Discriminant Analysis, Decision Trees, DL)
- Example of applications in biomedicine, business and much more
Competencies
Students are able to
- create and use simple and advanced scripts and notebooks in R.
- apply fundamental statistical learning methods to biological and medical problems.
- formulate simple medical and biological hypotheses and validate them using R and statistical methods.
Literature
- Gareth James et al.: An Introduction to Statistical Learning with Applications in R. Springer 2021
- Hadley Wickham, Garrett Grolemund: R für Data Science: Daten importieren, bereinigen, umformen, modellieren und visualisieren. O'Reilly 2017
- Victor Bloomfield: Computer Simulation and Data Analysis in Molecular Biology and Biophysics. An Introduction Using R. Springer 2009
- Daniel Wollschläger: Grundlagen der Datenanalyse mit R: Eine anwendungsorientierte Einführung. Springer 2017
- Uwe Ligges: Programmieren mit R. Springer 2008
Lecturer
Recommended Previous Knowledge
Empfohlene sind Grundlagen der Statistik.
-
Basic knowledge of data visualization is an advantage
-
Knowledge of other data science environments such as Python Anaconda is an advantage.
Module Details
| Semester |
1/2 |
| Lecture Language |
German |
|
Frequency
|
Summer Term
|
| Credit Points (ECTS)
|
5 |
| Course Coordinator |
Prof. Dr. Markus Gumbel |
| Duration |
1 Semester |
| Study Focus |
Medical Data Science (MDS)
|
|
Course Achievement
|
None |
|
Prerequisite for exam
|
Compulsory Assignment (PU) |
|
Exam
|
Written Exam 90 Minutes (K90) |
Weekly Hours (SWS)
| Lecture |
2 SWS |
| Exercises |
2 SWS |
| Sum |
4 SWS |
Work Load
| Lecture |
60 h |
| Self Study |
30 h |
| Assignments |
40 h |
| Exam Preparation |
20 h |
| Sum |
150 h |
