Study programme

Programme outline

First semesterSecond semester
First year 
  • Survey Data Analysis 7.5 EC
  • Multivariate Statistics 7.5 EC
  • Fundamentals of Statistics 7.5 EC
  • Computational Inference with R 7.5 EC
  • Introduction to multilevel modelling and psychometrics 7.5 EC
  • Causal Inference and Structural Equation Modelling 7.5 EC
  • Bayesian Statistics 7.5 EC
  • Introduction to Biomedical Statistics 7.5 EC
Second Year
  • Elective courses and Research Experience 15 EC
  • Research Seminar  5 EC
  • Markup Languages 2.5 EC
  • Preparation Master's Thesis 15 EC
  • Master’s Thesis  22.5 EC

Below, you will find an overview of courses from the current academic year of this Master's. This overview is meant to give you an idea of what to expect. The course offer may change in the coming academic year.

Year 1: Strong Foundation

In the first year, you will gain a strong foundation in research methodology and applied statistics. You will also explore how methodology and statistics can contribute to research performed in the behavioural, biomedical, and social sciences.

Year 2: Electives and (preparation for) Master's thesis

In the second year, you will start to specialise in the research area that interests you most. Typically, this will be in a collaborative project with other disciplines/organisations, where you will prepare for and write your thesis.

You can choose to pursue your own, free track or choose one of our predefined tracks. In addition, you will be involved in the statistical consultancy activities of our departments. 

Master's thesis

Students will be provided with an elaborate list of challenging thesis topics to choose from. Your Master’s thesis will take the shape of a scientific article, which may be published in an international journal.

Some thesis titles from previous years:

  • Handling the multiple testing problem for EEG analysis through Bayesian spatio-temporal models
  • How to Validate Regularized Regression models on Incomplete

  • Missing the Point: Non-Convergence in Iterative Imputation

  • Predicting Antibody Titers in Anti-Tetanus Donors

  • Multiple imputation in data that grow over time: A comparison of three strategies

  • Big Data Use in Official Statistics: It's About Time

  • Show me who you’re thinking of: modelling reverse correlation of faces

  • State-split approach for an extra time scale in multi-state models

  • Detecting Interaction Using a Two-way Multigroup Common Factor Model

  • Random forest algorithm performance under violation of independent, identically and uniformly distributed features

‘We are offered material that relates to a wide variety of fields and when it is possible we are pushed to formulate solutions and reason on our own as practitioners, later to realise our mistakes and learn how we made those mistakes.’