Mathematical Data Science

Data-driven methods are reshaping how we study natural and engineered systems, and this Master's programme treats data science as a rigorous mathematical discipline rather than just curve fitting. You will gain a solid theoretical grounding in the mathematics behind modern algorithms — from non-convex optimisation and numerical simulation to numerical linear algebra, stochastic processes, high-dimensional statistics and graph theory — alongside essential computer-science foundations like neural networks, distributed computing, cryptography and information retrieval.

The curriculum combines these core mathematical and computational courses with electives drawn from application areas such as AI, computational neuroscience, control engineering, cybersecurity and bioinformatics, so you learn both the theory and how to apply it. Studying at a smaller technical university means many courses are shared across programmes, enabling frequent interaction with peers from engineering, economics and other disciplines and opening chances to contribute to research in energy systems, neuroscience and sustainable technologies.

International students can take German language classes during their studies (while German-speaking students may choose to learn another language), which helps prepare for careers in industry or the public sector in Germany. Graduates go on to strong career paths in academia, industry and public service across sectors including IT and technology, health care, finance, consulting and resource management.

Key facts and requirements

• Degree awarded: Master of Science (MSc) in Mathematical Data Science
• Language of instruction: English
• Location: Cottbus, Germany (Brandenburg University of Technology Cottbus‑Senftenberg)
• Core topics: non-convex optimisation, numerical simulation, numerical linear algebra, stochastic processes, high-dimensional statistics, graph theory; neural networks, distributed computing, cryptography, information retrieval
• Application areas covered: AI, computational neuroscience, control engineering, cybersecurity, bioinformatics, plus projects in energy systems, neuroscience and sustainable technologies
• Study environment: small technical university with interdisciplinary courses shared across programmes (engineering, economics, etc.)
• Language support: German courses available for international students; German speakers may learn another language
• Career prospects: academia, industry, public sector (IT/technology, health care, finance, consulting, resource management)

For full details about application rules, entry requirements and course structure, please see: https://www.b-tu.de/en/math-data-science-ms.

This two-year curriculum (four semesters) leads to a 120 credit-point Master’s degree and combines a strong mathematical core with flexible electives and a small general-studies component. The program pairs rigorous coursework with seminars and an independent research thesis so you build both theoretical depth and applied problem-solving experience. Elective options are sizable, allowing you to tailor the degree toward theory, methodology or real-world data-science applications.

Core modules establish the mathematical backbone for advanced data-science work. Key compulsory units — Mathematical Foundations of Data Science, Seminar Mathematical Data Science, the Master’s Seminar and the Master’s Thesis — focus on rigorous modeling, proof-based understanding, critical evaluation of research, scientific communication and the ability to carry out an independent, substantial research project. The seminar formats train you to present, discuss and critique current research and to translate mathematical ideas into data-science solutions.

A large elective component (68 credits) is organized into three thematic complexes — Advanced Mathematical Methods in Data Science, Fundamentals of Data Science, and Data Science Applications — so you can deepen your expertise in advanced methods, consolidate core data-science techniques, or emphasize application-driven projects. A 6-credit General Studies module complements the technical training with broader skills and perspectives valuable for diverse careers or further academic study.

Requirements (curriculum breakdown)

• Duration: 4 semesters; total 120 credit points
• Mathematical compulsory modules: 46 credits, including:
  • Mathematical Foundations of Data Science
  • Seminar Mathematical Data Science
  • Master’s Seminar
  • Master’s Thesis
• Mandatory elective modules (total 68 credits) drawn from three complexes:
  • Advanced Mathematical Methods in Data Science
  • Fundamentals of Data Science
  • Data Science Applications
• General Studies module: 6 credits

Learning outcomes you can expect

• Deep, rigorous mathematical understanding underpinning modern data-science techniques
• Ability to apply advanced mathematical and statistical methods to data-driven problems
• Experience in critical reading, presentation and discussion of research through seminars
• Capacity to conduct independent research and communicate results via the Master’s thesis
• Opportunity to specialize toward theory, methodology or application through elective choices

Graduates are prepared for roles in academia, industry and the public sector where strong mathematical and computational skills are required. Typical career paths include research or development positions in IT and technology firms, data science and analytics roles in healthcare and finance, as well as consulting and resource-management positions that require quantitative modelling expertise.

The programme's combination of theory and application also positions graduates well for continuing into PhD study or working in interdisciplinary research groups (for example in energy systems, neuroscience or sustainable technologies).