Students are required to select two of the following seven areas of specialisation.

Details of the individual courses are listed in the Module Descriptions (Modulbeschreibungen, in German).

Statistical Inference

This area of specialisation provides a deep understanding of the theoretical and practical aspects of modern statistical methods to infer the properties of a population from observations. Students acquire the ability to formulate analytical problems and compute the appropriate statistical solutions accordingly. They are taught about the current state of research and are prepared to further develop and apply statistical methods to independent projects or within academic research. Students have the flexibility to choose the scope of their training in both theoretical and applied statistics and can explore several current research directions.

Econometrics

This specialisation focuses on the study of the development and application of statistical methods to draw valid conclusions from economic data. Advanced methods in the areas microeconometrics, panel data analysis, treatment effect analysis and time series analysis are the major components of the econometrics specialisation. The methods can be applied to economic data sets in project and seminar courses, thereby, students are able to assess current econometric research and conduct their own independent research projects.

Quantitative Methods of the Financial Markets

This specialisation teaches the relevant statistic and econometric methods and models which are of importance in the analysis of financial markets. The range of topics covered includes the pricing of options and other derivatives, time series models such as ARIMA and GARCH, analysis of high-frequency time series by means of point processes, and risk management models such as Value-at-Risk (VaR). The specialisation is designed as an introduction to the statistical instruments of the financial and insurance industries, the quantitative methods of the financial system and the statistical aspects of credit evaluation.

Survey Statistics

This area of specialisation focuses on quantitative data obtained from populations or institutions and its statistical analysis in theory and practice. In particular, students become familiar with important surveys as well as the methods needed to analyse survey data such as calibration, weighting, small sample estimation, and panel estimation, among others. Furthermore, methods on how to deal with missing data are also introduced. The specialisation equips students with the knowledge to work as survey statisticians in official statistics.

Applied Microeconometrics and Quantitative Economic Research

This specialisation introduces statistical methods in order to analyse mainly economic data. The courses focus on statistical and machine learning frameworks in the context of decision making, the connections between machine learning methods, statistics and econometrics and the analysis of treatment effects. The students study methodologies as well as their practical implementations in statistical programming languages, thereby, students are able to conduct their own projects and draw conclusions in economic contexts.

Statistics in the Life Sciences

This specialisation introduces Biometrics and Psychometrics with an emphasis on their applications. The courses in this specialisation focus on the methods relevant to medicine as well as psychology and challenges in these fields. In particular, methods for the analysis of censored data, small samples and dependent data, the design of epidemiological and clinical studies as well as multiple testing issues are all presented. Students are trained to analyse complex problems, prepare analytical support for important decisions and work on challenging projects, for example, in biomedical or psychological research or within the pharmaceutical industry.

Data Science

This interdisciplinary specialisation equips students with the ability to understand and deploy statistical and machine learning frameworks in order to analyse and visualise data as well as to develop predictive models to make data-driven decisions in various contexts. The modules of the Data Science specialisation focus on practical exercises that are applicable in real-world settings, through which students can develop, among others, an in-depth understanding of optimisation methods and predictive modelling. Students can apply their computational and statistical skills to design data-driven solutions and ultimately conduct their own projects.