The Master’s programme in Mathematical Statistics provides a broad spectrum of tools and methods for handling random phenomena occurring in scientific as well as industrial contexts. Within the programme, you can specialise in many different areas for different purposes, such as the modelling of economical, biological and environmental data.
You study at least 45 credits in mathematical statistics at the Master’s level and write a Master’s thesis of 30 credits. You can choose to take the remaining (at most 45) credits in e.g., mathematics or numerical analysis. You can also choose courses in other subjects, such as computer science or, if you are aiming for a career in a specific applied field, courses in that field. Examples include courses in economics, molecular biology and bioinformatics.
If you intend to proceed to a PhD, you should take courses with a high degree of theory content, while if you are aiming for a career outside academia, you should take courses that cover a wide range of statistical models and methods.
Stationary Stochastic Processes (7.5 credits)
Markov Processes (7.5 credits)
Mathematical Foundations of Probability (7.5 credits)
Time Series Analysis (7.5 credits)
Monte Carlo Methods for Stochastic Inference (7.5 credits)
Non-Parametric Inference (7.5 credits)
Stationary and Non-Stationary Spectral Analysis (7.5 credits)
Linear and Logistic Regression (7.5 credits)
Statistical Modelling of Extreme Values (7.5 credits)
Inference Theory (7.5 credits) or Design of Experiments (7.5 credits)
Non-Linear Time Series Analysis (7.5 credits)
Spatial Statistics with Image Analysis (7.5 credits)
Valuation of Derivative Assets (7.5 credits)
Financial Statistics (7.5 credits)
Statistical Modelling of Multivariate Extreme Values (7.5 credits) or other elective courses.
Master’s degree thesis (30 credits)
With a Master of Science in Mathematical Statistics, you have great opportunities to form an exciting career in, for example, the pharmaceutical industry, biotechnology companies or the banking and finance sector. Statistical methods are also of great importance for logistics, quality assurance and development in industry and organisations within the public sector.