The two year master in statistics is built on the Statistic B.Sc. and can be started in either the summer or winter semester. The working environment is always changing and developing, and that's why we teach necessary methods, knowledge and information with the aim to be able to work in the field of science, making sure students have scientific awareness and can trade it responsibly. 

Students who have already completed a B.Sc. in Data Analysis and Data Management or Mathematics can apply for this master under certain conditions. Exceptions can also be made on application.

This course allows master students to broaden their knowledge of mathematical statistics by taking elective courses in many specialized fields, such as non-parametric statistics, robust statistical processes or regression. As well as dealing with the core statistical methods, individual work is highly encouraged through different projects.

The application of statistics increasingly encourages autonomy, which is displayed in the use of special terminology, adapted methods and organisation. This is why you can choose a focus in biometrics, econometrics/empirical economy, technometrics or official statistics (EMOS - European Master in Statistics). You can also study without a focus.

The course is made up of eight modules with exams and six months spent working on a thesis. There will also be about 3 modules of a minor course, including exams as well.

The admission requirements for the masters course can be found in paragraph 3 of the masters examination information. Admission is possible through application to the examinations office of the statistics department.

Most courses are taught in German.

• Terms of Study (german)
• Module Handbook (german)
• Course Overview (german)
• Minor Subjects
• Affidavit (Mandatory! To be included in your Master Thesis)
  • Amendment to Affidavit (Also mandatory! Concerns the use of IT-/AI-supported language models. To be included in your Master Thesis as well)
  • List of positive examples (List of positive example how to use IT-/AI-supported language models in the preparation of theses)

Note: Older versions of these documents (enrollment before 2019) are collected on this page.