Programme Structure

The structure of the programme is very free. You can choose from a large number of restricted elective and elective courses to compose the programme as you choose. The courses offered seek to couple mathematical, statistical, and practical aspects, usually with a focus on a special type of study and the theoretical, and practical problems it entails.

You may choose to focus on one of the three disciplines (probability theory, theoretical statistics or data processing) and become, for example, a probability theoretician or obtain skills in statistical computing.

There are good opportunities for participating in external projects as part of your studies. You can also choose to collaborate with a company or an institution by doing a project in practice.

Study Abroad

Furthermore, it is possible to study abroad during your programme. You can choose to study for one or two semesters or for a shorter period, for instance attend a summer school course.

More information about studying abroad

Master's Thesis

The programme concludes with a thesis, where you work in depth with an academic problem. Often, the thesis has a practical point of departure, where the solution to the problem may of importance to many people.

Here are some examples of thesis topics:

• Survival Analysis - Analysis of Waiting Times
• Ruin Problems
• Markov Chain Monte Carlo Methods
• Cointegration - Stable Correlations in Explosive Processes
• Missing data - When It Is Informative that Data Is Missing

Programme Overview

Compulsory courses: 30 ECTS
Restricted elective courses: 30 ECTS
Elective courses: 30 ECTS
Master's thesis: 30 ECTS

One block each year equals nine weeks of study and 15 ECTS.

Restricted Elective Courses

Choose your restricted elective courses from the list below. Click on each course for a detailed description.

• Advanced Vector Spaces
• An Introduction to Large Deviations
• Applied Probability
• Brownian Motion
• Causality
• Computational Statistics
• Deep Learning
• Differential Operators and Function Spaces
• Financial Econometric Time Series Analysis
• Functional Analysis
• Geometry 2
• Inference for Stochastic Differential Equations*
• Interpretable Machine Learning
• Introduction to Extreme Value Theory
• Machine Learning A
• Machine Learning B
• Mathematical Modelling in Infectious Disease Epidemiology
• Models for Complex Systems
• Monte Carlo Methods in Insurance and Finance
• Numerical Optimization
• Online and Reinforcement Learning
• Point Processes
• Seminar in Statistics
• Semiparametric Inference*
• Stochastic Processes in Continuous Time
• Survival Analysis
• Targeted Learning
• Topics in Probability
• Topics in Statistics

* The course is not offered in the academic year 2024/25