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Statistics and Data Science in Practice


Admission requirements



In this course, emphasis is on the role of methodology, statistics and data science in the field of the Life and Behavioural Sciences. Good research methodology and valid statistical and data science methods are of vital importance for progress in this field. This course gives an overview of the role of statistics and data science in three broad research themes:

  • Clinical, Epidemiological, and Observational Medical Research. Leiden University Medical Center.

  • Plant/Animal/Human Genetics and Genomics, and Environmental Research. Wageningen University.

  • (Neuro)psychological Research. Leiden University.

Part of each theme are research visits to research institutes where an overview of the area is given with emphasis on current methodology and statistical applications. Additional background knowledge is provided by articles and lectures. Research visits include:

  • Leiden University Medical Center, Leiden

  • Wageningen University, Wageningen

  • Leiden Institute for Brain and Cognition MRI scanner, Leiden

Course Objectives

The overall aim of this course is to introduce students to important areas of application of statistics and Data Science methods in the Life and Behavioural Sciences. Students will also develop their communication skills (presenting, writing and working in teams). More specifically, students should, at the end of the course:

  • Have basic knowledge of the types of research performed in the different subfields in the Life and Behavioural sciences.

  • Have basic knowledge of research designs, and methods of the different subfields in the Life and Behavioural sciences, both for experimental research and observational research.

  • Be able to identify the prominent statistical and data science methods within a subfield.

  • Be able to critically assess the role of the statistical and data science methods within a sub-field (i.e. highlight advantages, and limitations).

  • Be able to compare the role of statistics and data science across different subfields (i.e. highlight similarities and differences).

  • Are aware of issues regarding messy and dirty (real-world) data.

Mode of instruction

The course consists of a mix of lectures, half-day research facility visits, and workgroups.

Assessment method

Students will work in small groups to prepare a final presentation on a specific statistical topic covering the three subfields. Each student also prepares an essay on this topic. The final grade will be based on the quality of the essay (50%) and performance on the presentation (50%). A resit possibility is scheduled in consultation with the teacher.

Lecture, research visit and workgroup attendance is obligatory. Details concerning this obligation will be announced at the first day of the course. If the attendance obligation is not met, the course has to be retaken the following academic year.


Literature will be specified during the course, no books are required.


w [dot] d [dot] weeda [at] fsw [dot] leidenuniv [dot] nl