In an increasingly complex and dynamic world, mathematics plays a central role in sustainable development. As an interface between theoretical modeling and practical application, it enables us to understand complex relationships and make data-driven decisions.

Concrete application areas include, for example, modeling climate risks in credit portfolios or analyzing default probabilities using logistic regression and machine learning. The combination of mathematical methods and artificial intelligence opens up new possibilities for quantifying sustainability risks and integrating them into existing risk models.

This combination of scientific depth and practical relevance is also reflected in this master’s program, which specifically prepares students for the challenges at the intersection of sustainability, business, and data science—whether for a career in business or an academic path leading to a Ph.D.

The examination regulations set forth the general framework for examinations at our university. A course catalog provides an overview of the various modules that must be taken as part of a degree program.

You can download the examination regulations and the course catalog here:

General Provisions  - Examination Regulations - Module Manual

The program combines academic depth with a strong practical focus. Required modules such as measure and integration theory, time series analysis, and management skills form the foundation, supplemented by elective courses from the specialization tracks. You decide whether to specialize or maintain a broad focus. This degree also opens the door to pursuing a Ph.D. Here, you will acquire the key competencies to actively shape the future.

The list of required elective modules includes the following modules, among others:

In addition, any of the modules listed in the examinations regulations and course catalog under the course codes

• Topics in Sustainable Modern Business Mathematics,
• Topics in Sustainable Operations Research, Stochastics, and Quantitative Sustainable Management Support,
• Topics in Mathematical Data Science and Business Informatics, and
• Advanced general mathematical theories and specialization modules

can be used for individual specialization (see Appendix 2, No. 3).