The structure of the programme is very free, and you can compose your course of study 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 aspects 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.
It is also possible to study abroad during your degree. You can choose to study abroad for one or two semesters or for a shorter period of time, for instance attend a summer school course.
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.
Possible 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
The programme is structured as follows:
|Block 1||Block 2||Block 3||Block 4|
|Year 1||Restricted elective course||Restricted elective course||Restricted elective course||Restricted elective course|
|Regression||Statistics A||Statistics B||Project in Statistics|
|Year 2||Elective course||Elective course||Thesis|
|Elective course||Elective course|
One block each year equals nine weeks and 15 ECTS
Restricted elective courses
Choose your restricted elective courses from the lists below. Click on each course for a detailed description.
- Advanced Probability Theory 1
- Computational Statistics
- Advanced Vector Spaces
- Econometrics 2: Statistic Analysis of Econometric Time Series
- Advanced Topics in Machine Learning
- Monte Carlo Methods in Insurance and Finance
- An Introduction to Large Deviations (the course is not offered in the academic year 2021/22)
- Advanced Vector Spaces
- Demography and Mortality
- Graphical Models (the course is not offered in the academic year 2021/22)
- Introduction to Extreme Value Theory
- Advanced Probability Theory 2
- Survival Analysis
- Machine Learning
- Optimization in Data Science
- Functional Analysis
- Geometry 2
- Numerical Optimization
- Semiparametric Inference (the course is not offered in the academic year 2021/22)
- Machine Learning Methods in Non-Life Insurance
- Applied Probability
- Topics in Statistical Genetics
- Differential Operators and Function Spaces (DifFun)
- Models for Complex Systems (ModComp)
- Applied Programming
- Structural Equation Models (the course is not offered in the academic year 2021/22)
- Mathematical Models in Epidemiology
- Online and Reinforcement Learning (OReL)