Students are required to select two of the seven electives of specialization.

Details to the individual tracks can be found in the Module Descriptions (Modulbeschreibungen, in German).

Statistical Inference

This track 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 and compute approximate solutions to statistical problems. They are knowledgeable 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.

Data Science

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

Econometrics

This track 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 specialization track. The methods can be applied to economic data sets in project and seminar courses. Thereby, students are enabled to assess current econometric research and conduct their own independent research projects.

Quantitative Methods of the Financial Markets

This specialization track 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 track is designed as an introduction to the statistical instruments of the financial and insurance industries, the quantitative methods of the financial system and statistical aspects of credit evaluation.

Applied Microeconometrics and Quantitative Economic Research

This track introduces statistical methods to analyze 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.

Survey Statistics

This track focuses on quantitative data obtained from populations or institutions and its statistical analysis in theory and practice. In particular, students study sampling methods such as Monte-Carlo sampling, get to know important surveys as well as methods to analyze survey data such as calibration and weighting, small sample estimation, panel estimation, among others. Furthermore, methods to deal with missing data are introduced. The track equips students with the knowledge to work as survey statisticians in official statistics.

Statistics in the Life Science

This track introduces Biometrics and Psychometrics with an emphasis on applications. The courses focus on 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 presented. Students are trained to analyze complex problems, prepare analytical support for important decisions and work on challenging projects e.g. in biomedical or psychological research or within the pharmaceutical industry.