Due to the Corona virus education methods or examination can deviate. For the latest news please check the course page in Brightspace.


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Introduction to Behavioural Data Science


Admission requirements

Not applicable.


The course is build around three themes. First, we will discuss the methodology of scientific research involving human behavior, covering the following aspects: deriving a verifiable research idea, selecting data collection methods, and determining reliability and validity. In part two, we will introduce different statistical philosophies for analyzing behavioral data. The following topics will be addressed: descriptive statistics, frequentist hypothesis testing, Bayesian hypothesis testing, cross-validation, and design analyses. To this end, we will use the statistical programming language R. Finally, in part three, meta-scientific themes inclunding pre-registration, reproducibility, and replicability will be discussed.

Course objectives

  • Understanding key concepts regarding methods and techniques of behavioral data science.

  • Applying different statistical philosophies (i.e., frequentist hypothesis testing, Bayesian hypothesis testing, and cross-validation).

  • Analyzing data in R.

  • Understanding key meta-scientific concepts.


Timetable Artificial Intelligence

Mode of instruction

A two-hour lecture and a two-hour work group session per week.

Assessment method

The assessment involves

  • A written, clossed-book exam consisting of 40 multiple choice questions with four alternatives each, covering both theoretical knowledge as well as statistical calculations discussed in the lectures, and work group sessions.

  • An R skills assignment covering the various aspects of students’ skills in working with R as well as in describing and interpreting statistical output.

The final grade is a weighted average of the examination grade (70%) and the grade for the R skills assignment (30%). However, in order to pass the course, students must get a grade of 5.5 or higher on both parts (henceforth partial grades). Students have the opportunity to retake the exam and/or the R skills assignment, if their respective partial grade(s) were below 5.5. If, after the resit, one or both partial grades are below 5.5, the student needs to retake the entire course.
The teacher will inform the students how the inspection of the exams will take place.

Reading list

Course material includes slides, exercises, and articles that will be made available via the online course platform.


Aanmelding voor vakken verloopt via uSis. Hiervoor is de uSis-code van het vak nodig, die te vinden zijn in de Studiegids. Meer info over het inschrijven voor vakken of tentamens is hier te vinden.


In MyTimetable kun je alle vak- en opleidingsroosters vinden, waarmee jij je persoonlijke rooster kunt samenstellen. Onderwijsactiviteiten waarvoor je in uSis staat ingeschreven, worden automatisch in je rooster getoond. Daarnaast kun je My Timetable gemakkelijk koppelen aan een agenda-app op je telefoon en worden roosterwijzigingen automatisch in je agenda doorgevoerd; bovendien ontvang je desgewenst per e-mail een notificatie van de wijziging.

Vragen? Bekijk de video-instructie, lees de instructie of neem contact op met de ISSC helpdesk.


Inschrijving voor vakken verloopt via uSis. Wanneer je je hier inschrijft voor een bepaald vak krijg je automatisch ook toegang tot de omgeving van dit vak via Brightspace.

Voor meer informatie over Brightspace kun je op deze link klikken om de handleidingen van de universiteit te bekijken. Bij overige vragen of problemen kan contact opgenomen worden met de helpdesk van de universiteit Leiden.


Dr. T.D.P. Heyman t.d.p.heyman@fsw.leidenuniv.nl
Dr. S.M.H. Huisman s.m.h.huisman@fsw.leidenuniv.nl

Riet Derogee, coordinator