Data Science for Society and Business

The master program Data Science for Society and Business study program teaches international students with strong backgrounds in social sciences (e.g. business, economics, demography, media studies, political science, psychology, sociology,) how to make use of rapidly growing digital data resources and new computational tools and methods to potentially solve the challenging interdisciplinary problems in their professional or academic field.

The program may also be interesting for students with a background in humanities, natural or technical science who want to focus on innovative social data analytics and pressing social and business questions emerging from digitization.

Students of the DSSB program thereby benefit from a broad course offering in various social, data and business sciences and the close cooperation with computer sciences, environmental and life sciences.

In addition, the program also promotes individual diversity and specialization. DSSB students can pick and choose course offerings from the 3 elective tracks based on their individual interests and career plans

1. The Society and Business Track covers computational social science approaches, smart city, and transport concepts as well as principles of consulting, sustainable economics, and supply chain finance.
2. The Health & Environmental Track connects socially relevant data science questions with insights and techniques from the natural sciences (health, medical, environmental sciences)
3. The Data Science Track allows students with a strong mathematical or computing background to dive deep into data mining, data analytics and machine learning

At the end of the 2-year program DSSB graduates will have expertise in digital contents and data science skills to responsibly and capably solve core problems in future organizations and digital societies.  

• They can identify, analyze, interpret and critically access the social causes and consequences of digital transformation of societies in all of its legal and ethical implications and aspects
• They are able to apply cutting-edge analytical and quantitative skills to correctly model and interpret scientific results, to make valid predictions and to derive thoughtful conclusions and interventions for pressing social and business problems
• Students have learned to manage big data, develop statistical models and convincingly present them to a science and non-science audience by applying plausible writing, communication and presentation techniques and visualization tools
• They will be able to program well in at least one computer language and know about state-of-the-art computational and software tools