Methodology and Statistics for the Behavioural, Biomedical and Social Sciences

What characterises a good research question, and how can we align it with data collection and statistical analyses to obtain meaningful results? How can we strengthen the reliability and validity of our findings through refining existing techniques, integrating approaches, or developing new methods? And how can we communicate these challenges and findings effectively in interdisciplinary teams, where data literacy and perspectives vary?

State-of-the-art research in methodology, 
data science, and statistics

Modern society is highly data-driven. Everything is monitored and measured, yet there are major challenges to finding optimal ways to design research studies, analyse data, extract the most valuable information from data, and communicate the results. This two-year Master's programme trains students to become researchers, data scientists and data analysts who can bridge the gap between fundamental statistics underlying modern data analysis and empirical research in the behavioural, biomedical, and social sciences – both within and beyond academia.

Programme objective

In this programme, you are trained to solve real-world problems by applying, evaluating, and developing methodological and statistical approaches within an interdisciplinary setting. In pursuing this goal, you will learn to:

• Define a clear research question, and link this to a particular research design and statistical analysis.
• Understand how data collection, sampling, experimental design, and measurement impact data quality, which analyses you can do, and what conclusions you can draw.
• Work with a broad array of statistical and machine learning methods for description, explanation, prediction, and causal inference.
• Implement statistical and machine learning methods using various software packages.
• Write code and set up computer simulations to assess the performance of different analysis approaches and develop innovative methods that improve upon state-of-the-art techniques.
• Communicate and collaborate in multidisciplinary teams of researchers and practitioners to plan, collect, explore, analyse, summarise, and present data products in a reproducible, accessible, and open way.

Our students are the next generation of statisticians, data analysts, and data scientists. As such, we aim to provide a rich learning environment, that will help you fulfill your potential. We work in small groups so there is plenty of room for personal attention and our lecturers are top scientists. So rest assured you will learn from the best!