Data Analysis and Visualization in R

Module IN2339

Credit: 6 ECTS.


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When and where?

This lecture is given in the winter term.

Virtual lectures every Tuesday from 14:00 - 16:00 via Zoom. Additionally, recordings will be available online. 

Exercises will also be held online via Zoom. For this we have four sessions at two time slots:

- Thursday, 14:00 - 17:00


- Friday, 09:00 - 12:00

The exercises will not be recorded. The links for the Zoom meetings will be available in Moodle. For Moodle access a registration to the course via TUM online is needed. Please make sure that you register twice in TUMonline, for the lecture and also for the separate exercise. Unfortuntaley we can only offer our exercise to a limited amount of people so make sure you register in time.


This module for students in bioinformatics, master students of Data Engineering and Analytics, and master students of Biomedical Computing teaches methodologies and good practice of data science using R. The lecture is structured into three main parts, covering the major steps of data analysis:

1. Get the data: how to fetch, and manipulate real-world datasets. How to structure them ("tidy data") to most conveniently work with them.

2. Look at the data: basic and advanced visualization techniques (grammar of graphics, unsupervised learning) will allow students to navigate and identify interesting signal in large and complex datasets and formulate hypotheses.

3. Conclude: concepts of statistical testing will allow concluding about the raised hypotheses. Also methods from supervised learning will allow to model data and build accurate predictors. Each week, the lecture is accompanied with exercises. During the exercises, combinations of the concepts seen during the lecture will allow performing more involved data analysis tasks. Students generate report that embed code and analysis. Two more advanced case studies complement the course. Many examples will stem from applications in genomics, but no pre-requisite in this domain is necessary.

Required background

Experience with programming of any language. The theoretical aspects of data analysis are kept low in this module. However, basics in probabilities are required. See our companion module "Statistical modelling and machine learning" to complement it.

Chapters 13-15 ("Introduction to Statistics with R", "Probability" and "Random variables") of the Book "Introduction to Data Science" make a good refresher. Make sure all concepts are familiar to you. Check your knowledge by trying the exercises. 

Recommended reading

R for Data Science, by Garrett Grolemund and Hadley Wickham

Modern Dive, by Chester Ismay and Albert Y. Kim

Introduction to Data Science, by Rafael A. Irizarry. 


Bring a laptop with RStudio installed, a free programming interface for the R language.


R programming basics, report generation with R markdown Importing, cleaning and organizing data (tidy data) Plotting and Grammar of graphics Unsupervised learning (hierarchical clustering, k-means, PCA) Drawing robust interpretations (empirical testing by sampling, classical statistical tests) Supervised learning (regression, classification, cross-validation)


The final exam is a 2 hours written exam. The mark will be the one of the final exam plus bonus points for the home works and case studies.

Teaching team

This lecture is given by a team of scientists with long experience in high-dimensional data analysis in the field of genomics: Prof. Julien Gagneur and members of his lab. Dr. Matthias Heinig, Group leader, Institute of Computational Biology, Helmholtz center

If you have any questions, please contact us via Email to

If you cannot register for the re-exam please contact the secretary of your study program. We cannot register students!!!!