Data is everywhere but to retrieve the valuable insights requires important analytical skills. The large number of active programmers creating R packages makes R suitable for a range of data analysis techniques, from basic hypothesis testing to generalized linear regression, and multivariate analysis such as principal component analysis, factor analysis, or clustering. You will apply what you have learned right away in short exercises using Rmarkdown. You will be graded using an assignment in which you will learn to deal with messy data and integrate the knowledge you obtained in the exercises. The course is highly intensive as it focuses both on interpreting statistics while also learning to program in R.
Course overview
- Course dates: 6-17 July 2026 (excluding arrival and departure days)
- Attendance: In-person
- Forms of tuition: Lectures, exercises, self-study
- Forms of assessment: Written assignment
- See the course curriculum
Course level
- Level: Final year Bachelor's
- English language requirement: B2 level or higher (equivalent to IELTS 6.5)
- See the entry requirements
Workload
- Credits: Equivalent to 3 ECTS
- Contact hours: 45
Lecturers
- Coordinating lecturer: dr. Meike Morren