The Master of Science in Statistics is an accredited consecutive graduate course of studies and covers a course load of 120 credit points (ECTS) with an average period of study of four semesters.

Compulsory Area (62 CP)

• Stochastik I (10 CP)
• Econometric Methods or Methoden der Statistik (10 CP)
• Multivariate Statistical Analysis or Multivariate Verfahren (6 CP)
• Advanced Econometrics or Statistik für Fortgeschrittene (6 CP)
• Master's thesis (30 CP)

Interdisciplinary Area (10 CP)

Specialisation Area (48 LP)
Students are required to select two of the seven areas of specialisation.
Details of the individual courses are listed in the Module Descriptions ("Modulbeschreibungen", in German).

Statistical Inference
This focuses on modern statistical methods to infer population properties from data. Students learn to formulate problems, compute solutions, and apply methods in research and projects, with flexible training in theory and application.

Econometrics
This covers statistical tools to analyse economic data. It emphasises microeconometrics, panel data, treatment effects, and time series. Students apply methods in projects, assess current research, and conduct their own research.

Quantitative Methods of Financial Markets
This teaches statistical and econometric tools for financial market analysis. Topics include option pricing, ARIMA, GARCH, point processes, and risk models like VaR. It prepares students for finance and insurance roles.

Survey Statistics
This focuses on analysing population data and covers survey methods like calibration, weighting, small sample estimation, missing data, and panel analysis. It prepares students for roles in official statistics.

Applied Microeconometrics and Quantitative Economic Research
This introduces methods for analysing economic data using statistics and machine learning. The emphasis is on decision-making, treatment effects, and practical application with programming. It enables independent project work.

Statistics in the Life Sciences
This covers Biometrics and Psychometrics with applications in medicine and psychology. Includes methods for censored/dependent data, small samples, clinical study design, and multiple testing. It prepares students for roles in biomedical or pharmaceutical research.

Data Science
This equips students with statistical and machine learning skills for data analysis, visualisation, and predictive modelling. It focuses on real-world applications, optimisation, and project-based learning for data-driven solutions.

• Integrated/optional study abroad unit(s)

The study plan recommends the third semester as particularly suitable for an optional study period at a university abroad. To simplify the recognition of academic achievements and examinations completed at the foreign university, it is advisable to conclude a learning agreement in advance.

Internships are not compulsory, but they are encouraged. An internship can be recognised in the interdisciplinary elective area with up to 10 CP.