M.Sc. Data Science in Business and Economics

Why Data Science?

Data Science continues to be a vibrant, interdisciplinary and rapidly growing field. More and more firms and institutions are hiring data scientists, and data scientist has been one of America's top jobs for many years in a row according to Glassdoor.

Data scientists must be able to acquire, understand, and analyze complex and unstructured data, to interpret the meaning of the results, and to communicate the results to relevant stakeholders. Hence, Data Science requires a unique combination of (1) econometrics and Machine Learning, (2) programming skills, and (3) domain knowledge.

The surging interest in AI technologies, such as Large Language Models, highlights the need for firms and managers to possess a good understanding of their capabilities and limitations. This emphasizes the importance of data science education in empowering professionals with the critical thinking and technical skills required to use these tools responsibly, customize solutions them to solve specific problems, and to make sound decisions in a data-centric world.

Why this program?

This four-semester M.Sc. program reflects these requirements to provide its graduates with a unique skill set including state-of-the-art development in Machine Learning. Students who enroll in this program specialize in econometrics and select further modules from a broad set of options on different aspects of business and economics. On top of that, students complete modules on data science techniques, which includes programming in R and Python as well as the possibility to enroll in courses in Machine Learning in collaboration with the computer science department.

A unique element of the program is the Data Science Project, which is typically part of the 3. semester. Students form teams and design and execute a data science project including data collection, data cleaning, modeling and analysis, and presentation. The final product is usually an App or a dash board that allows the user to explore the problem and the solutions. Example projects include the following:

• Students trained a model to evaluate the safety of streets at night. The outcomes of this model served as the foundation for a navigation app that helps users choose the safest routes during nighttime.

• Students developed a model to detect exits on highways (Autobahn) and analyze the adjacent surface structure to pinpoint potential locations for solar parks on underutilized land.

• Students created a machine-learning pipeline to recognize surface structures in major cities. This was combined with satellite-measured surface temperature data to identify urban heat islands.

• Students designed a bicycle navigation app that employs a specially developed machine learning pipeline to identify bike-friendly routes.

Why Tübingen?

The University of Tübingen, along with its surrounding ecosystem, stands out as a leading hub for Machine Learning and AI research in Europe. It is home to notable institutions such as the Machine Learning Cluster of Excellence, the Tübingen AI Center, the Max-Planck-Institute on Intelligent Systems, and Cyber Valley. This creates a great environment rich in advanced research, diverse study programs, and a wealth of institutions dedicated to Machine Learning. This unique combination is rare globally, providing our students with unparalleled opportunities. Our faculty actively contributes to and benefits from this thriving environment, e.g., with several professors being members of the Machine Learning Cluster of Excellence

If you're passionate about Machine Learning, AI, Data Science, and their applications in business and economics - come study with us!