R for Data Science

Lecturer (assistant)
  • Nuno Busch
  • Edo Octavianus
TermSommersemester 2024
Language of instructionEnglish
Position within curriculaSee TUMonline


At the end of the module students will know and understand commonly applied methods in the field of data science. They are capable of applying these methods to novel data sets and problems, and know how to independently structure and implement data-analytic projects. They are familiar with the open scource programming language R, the graphical user interface RStudio, as well as with common packages and their applications.


Generating insight from raw data is an essential skill across various scientific disciplines and applied fields. Among the core competencies of a data scientst are structuring projects and workflows, importing, preparing and transforming data sets, common programming methods (such as iteration, functions, conditionals), visualizing and modeling data, and communicating the results in a comprehensible and insightful manner. The module equips students with a solid foundation of such common methods in the field of data science. These methods are demonstrated and practiced using the open source programming language R, associated packages (especially the "tidyverse"), as well as the graphical user interface RStudio.

Teaching and learning methods

Based on the suggested literature students will give short presentations, introducing the class to methods of programming, data wrangling and data analysis. The students are asked to integrate interactive elements and concrete demonstrations in these presentations. In exercises (solved in small groups or individually) the class practices and consolidates the implementation of the introduced methods by applying them to concrete data sets.

Recommended Literature

Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc.