Universität Wien
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300127 UE Introduction to R and Statistics (2026S)

data analysis and modelling

5.00 ECTS (3.00 SWS), SPL 30 - Biologie
Continuous assessment of course work

This course belongs in module MNB2 to subject area Data Analysis and Modelling.
Moodle

Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 16.04. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 30.04. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 28.05. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 11.06. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 18.06. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
  • Thursday 25.06. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1

Information

Aims, contents and method of the course

In this R course, I provide lectures, and practical examples to students to be able to undertake R as a widely used language to perform basic to intermediate statistical data analysis, and data visualisation. No prior knowledge in R is required.

Assessment and permitted materials

Each lecture is consisted of teaching, practice and working on assignments. Students need to finish the assignment and submit it by the end of the course, which will be the basis of the evaluation.

Minimum requirements and assessment criteria

Computers with installed R and RStudio will be provided to student during the course, however, students may feel free to bring their own laptop with them. There will be assignments in each lesson. In total 6 assignments will contribute 100% towards the final grade. A minimum final normalized score of 50% is necessary for course completion. As this is a practical course, presence is required, meaning that minimum 80% of presence is required to complete the course.

Examination topics

There will be no extra exam. In each lesson, students need to submit an assignment, based on which they learn. The final note is based on these 6 assignments.

Reading list

For achieving the learning objectives in a basic R programming course, students can rely on introductory textbooks, online tutorials, instructor-provided materials, and reputable online platforms like Coursera, DataCamp, etc. In addition, the literature below are recommended.

- Nathaniel D. Phillips. YaRrr! The Pirate’s Guide to R, 2017 ( https://bookdown.org/ndphillips/YaRrr/YaRrr.pdf )
- W. N. Venables, D. M. Smith, R Core Team. An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics Version 3.5.2, 2018 ( https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf )
- Trevor Martin. The Undergraduate Guide to R - A beginner‘s introduction to the R programming language ( http://www.biostat.jhsph.edu/~ajaffe/docs/undergradguidetoR.pdf )

Association in the course directory

MNB2

Last modified: Mo 27.07.2026 09:27