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300127 UE Introduction to R and Statistics (2026S)
data analysis and modelling
Prüfungsimmanente Lehrveranstaltung
Labels
This course belongs in module MNB2 to subject area Data Analysis and Modelling.
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Donnerstag 16.04. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
- Donnerstag 30.04. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
- Donnerstag 28.05. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
- Donnerstag 11.06. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
- Donnerstag 18.06. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
- Donnerstag 25.06. 08:00 - 14:45 Seminarraum 1.3, Biologie Djerassiplatz 1, 1.005, Ebene 1
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
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.
Art der Leistungskontrolle und erlaubte Hilfsmittel
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.
Mindestanforderungen und Beurteilungsmaßstab
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.
Prüfungsstoff
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.
Literatur
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 )
- 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 )
Zuordnung im Vorlesungsverzeichnis
MNB2
Letzte Änderung: Mo 27.07.2026 09:27