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636-0119-00L 6 Credits MSC D-BSSE
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Introduction to Statistics and R

Lecturers & Examiners: Dr. Jack Kuipers
Students of the CBB master's programme are welcome to join this course, but the course cannot be counted towards the regular master's degree in CBB. If assigned with admission, it can be taken as an additional requirement course by CBB students.
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Last Updated: 2026-02-05 15:34:27

Abstract

This course offers a practical introduction to the fundamentals of data analysis and R

Objective

To acquire the statistical understanding to design an appropriate analysis and the practical skills to implement the analysis in R and present the results.

Content

Data analysis is fundamental for arriving at scientific conclusions and testing different hypotheses. This course offers a hands-on introduction to statistical analyses including: exploratory data analysis, testing differences in populations, p-values, power calculations, multiple testing, confounding, linear regression, maximum likelihood, model selection, and logistic regression; along with the fundamentals of R programming including markdown and data handling with the tidyverse.

Resources

Lecture Notes

Lecture slides will be available

General Information

Language
English
Levels
MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
oral 20 minutes
Final grade: 50% oral examination, 50% project work.Project work has to be re-done in case of repetition.The course includes compulsory continuous performance assessments in the form of project work/assignments, which constitute 50% of the final grade.

Course Components

Type Title Time & Place Hours
lecture with exercise Introduction to Statistics and R
Attention: Lecture starts Thursday, September 24 Lecture takes place in Basel, room Misrock (BSA E46) and is being recorded. The number of seats in the classroom is limited to 22. Students of the D-BSSE master programmes are prioritized. If you plan to attend the course in classroom in Basel, please send an email to:
  • Thu 16:15-19:00 (BSA E 46)
3 h weekly
independent project Introduction to Statistics and R
Project Work (Compulsory continuous performance assessments), no fixed presence required
No time listed 2 h weekly

Offered In

  • Biotechnology Master
    • Electives (The electives list in the ETH course catalogue is an open list, and the courses listed in the ETH course catalogue provide just examples for possible elective courses, e.g. a selection of eligible courses. Students are expected to look for relevant courses in the ETH and University of Basel course catalogue and ask their mentor for approval. Courses from the advanced course category may also be taken as electives. We particularly recommend browsing the University of Basel course catalogue for elective courses of relevant master's degree programes (using the filter "programe structure" on the course catalogue website), such as for example: Biomedical Engineering, Chemistry, Drug Sciences, Epidemiology, Infection Biology, Molecular Biology, Nanosciences)