24th-25th-26th September 2024


9 am - 1 pm


Dr. Paul Schmidt

Number of Participants

7 to 15 participants





Target Group

This course is open to individuals from all faculties. It is aimed at doctoral candidates in the initial, working, or final phase of their dissertation as well as PostDocs.   

Participation Fee*

Free of charge 

Course Description

R is an open source programming language for statistical computing and graphics. It provides a large number of generic functions for statistical analysis, but also many specialised packages supporting advanced methods, for example multivariate statistics, linguistic analysis or the work with spatial data and databases. The course gives a basic introduction into working with R. It is possible to bring data from your own research and use it for exercises.


  • Statistics:
    • Linear models
    • (Multifactorial) Analysis of Variance (ANOVA)
    • Mean comparisons with post-hoc tests
    • Critical discussion on p-values and significance
    • Critical discussion on t-tests or Tukey tests
    • Experimental designs (block design, split-plot design)
    • Preliminary look: Mixed models
  • Data processing and preparation:
    • Efficient and controlled analysis of multiple datasets
    • Professional creation of various graphs; use of packages like ggplot2, desplot
    • Reproducible analyses (including an introduction to R Markdown)

Information about the lecturer

I completed my PhD in Biostatistics at the University of Hohenheim in 2019 and have been working as a Data Scientist in the Life Sciences at BioMath ever since. Additionally, I have conducted over 40 workshops in R and Python for beginners and advanced users. I enjoy when complicated methods are well explained, when results are presented concisely and vividly, and when R/Python delivers results as if by magic. More here: