Description
In 1987, a famous British statistician, George Box, proclaimed “Essentially, all models are wrong, but some are useful”. Today, companies like OpenAI or Google don’t fiddle with models that might give bad predictions, they simply extract relevant information from abundant data with no use for models at all. And yet, data science is far more than just curve fitting for those who are too lazy to do the “real” science. It is real science! It is likely that physical models for numerical weather prediction or protein folding will still be needed in the future and that the natural sciences will not cease to exist. So, what we are currently seeing is only the beginning of the integration of data mining and powerful large language models with models from physics, chemistry or biology, with no end in sight!
Why studying Mathematical Data Science?
The M.Sc. programme in Mathematical Data Science features comprehensive training in the mathematical foundation of Data Science algorithms. This includes courses in non-convex optimization and numerical simulation, numerical linear algebra, stochastic processes, high-dimensional statistics, or graph theory, but also courses that provide the computer science fundamentals, such as neural networks, distributed computing, cryptography,or information retrieval. The core curriculum is accompanied by numerous courses from related application fields, such as AI, computational neuroscience, control engineering, cybersecurity,or bioinformatics, where you will learn how to apply data science algorithms.
The M.Sc. programme in Mathematical Data Science is for you if you want to see behind the curtain and understand e.g.,
- how big machine learning models are trained,
- how data mining works (or when and why it might fail),
- how power grids or traffic networks can be managed under uncertainties.
But if you are merely interested in applying off-the-shelf data analytics tools to given problems, you better choose another study programme.
Why studying M. Sc. Mathematical Data Science at BTU Cottbus-Senftenberg?
Studying at a small technical university, with many courses shared between different study programmes, you will quickly get in touch with students from other disciplines (e.g. engineering, economics). You will also learn about their projects and the numerous opportunities at BTU Cottbus-Senftenberg to apply data science methods to do cutting-edge research in energy systems, neuroscience, or sustainable technologies.
If you are and international student, you will have the opportunity to take German courses, which gears you up for a future career in industry or the public sector.
(German speaking students may learn another language.)
Professional Fields of Activity
The rewards for studying Mathematical Data Science are excellent career opportunities in academics, industry or the public sector, with application fields ranging from IT and technology to health care, finance, consulting, or resource management. Students who are merely interested in applying off-the-shelf data analytics tools to given problems better choose another study programme.
Applications and Admissions
Admissions Requirements
- A first professionally qualifying degree (at least a Bachelor's degree) in a subject-related degree programme.
- The degree is sufficiently close in content if the mathematics course has a comparable scope to the Bachelor's degree programmes in Mathematics or Businessmathematics at BTU Cottbus-Senftenberg.
- Certificate of English language proficiency as described in the language requirements. Only valid certificates are recognized, »medium of instruction certificates« are not accepted.
- For applicants with a German university entrance qualification, the Abitur certificate can serve as sufficient proof.
