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University of Oldenburg
03.08.2020 23:02:52
neu720 - Statistical programming in R (Complete module description)
Original version English Download as PDF
Module label Statistical programming in R
Module code neu720
Credit points 6.0 KP
Workload 180 h
1,5 SWS Lecture (VO)
Total workload 68h: 28h contact / 20h background reading / 20h exam preparation
2,5 SWS Supervised exercise (UE):
Total workload 113h: 28h contact / 20h background reading / 65h exercise solving
Faculty/Institute Department of Neurosciences
Used in course of study
  • Master's Programme Biology (Master) >
  • Master's Programme Biology (Master) >
  • Master's Programme Neuroscience (Master) >
Contact person
Module responsibility
Authorized examiners
Entry requirements
Skills to be acquired in this module

+ Social skills
+ Interdiscipl. knowlg.
++ Maths/Stats/Progr.
+ Scientific English

students learn the use of the software R in application scenarios
students learn to actively "speak" the programming language R
students practice statistical data analysis with R
Module contents
The lecture gives an intuitive introduction into the use of the statistics software R. We start by introducing the basic handling of R and the syntax of its programming language. We use those to obtain the first statistical analyses from R. The next important step is to create informative graphics to represent the statistical results. Finally, we look into programming concepts that allow for more complex statistical analyses.
Reader's advisory
Uwe Ligges - Programmieren mit R (2008) Springer.
R Core Team - R: A language and environment for statistical computing (Reference Manual)
Simon N. Wood - Generalized Additive Models: An Introduction with R (2006) Chapman & Hall
Language of instruction English
Duration (semesters) 1 Semester
Module frequency annually , summer term
Module capacity 24
Reference text
Recommended previous knowledge / skills: basic statistical knowledge including regression analysis
Modullevel ---
Modulart Wahlpflicht / Elective
Lern-/Lehrform / Type of program
Vorkenntnisse / Previous knowledge
Course type Comment SWS Frequency Workload attendance
Lecture 2.00 SuSe 28 h
Exercises 2.00 SuSe 28 h
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
after the course
practical exercise