Mathematics in Data Science

This English-taught Master's concentrates on building a rigorous mathematical foundation for work in Data Science and Artificial Intelligence. The programme trains students in advanced techniques for data analysis and processing and in adapting complex mathematical models to practical, real-world problems. You will learn how to combine and refine models into predictive and classification systems, and how to evaluate the strengths and limitations of different algorithms so you can design effective, problem-specific solutions.

The curriculum puts particular emphasis on the mathematical underpinnings of methods used in machine learning, statistics, optimisation and data representation theory, while maintaining a strong link to applied work. Theory is balanced with hands-on study in areas such as data analysis, data engineering and machine learning, preparing you to implement and validate sophisticated methods on real datasets.

Alongside the core technical training, the programme includes interdisciplinary modules—foreign language instruction, introductory law courses and seminars on the social and political dimensions of data science—so you gain broader communication skills and an understanding of legal, ethical and societal implications. Graduates leave equipped for careers in industry or research, or for further doctoral study, with both the theoretical depth and applied skills expected in modern data science roles.

Core components and requirements (overview)

• Advanced mathematical foundations for data-driven methods
• Machine learning, statistical methods and optimisation techniques
• Theory of data representation and algorithm analysis
• Practical coursework and projects in data analysis, data engineering and machine learning
• Interdisciplinary classes: foreign language training, introductory law, and courses on social/political aspects of data science

Overview

This two-year (four-semester) Master's builds a solid, mathematically rigorous foundation for data science, covering the core stages of working with data: acquisition, mathematical modelling and statistical or algorithmic classification. Teaching combines traditional lectures with hands-on practical courses and seminars, and the curriculum gives you room to shape your pathway to match your research or career interests. Throughout the programme you also have opportunities to conduct independent research, culminating in a research-based Master's thesis in the fourth semester.

Alongside the technical and theoretical core, the programme deliberately weaves in discussion of the social and ethical implications of data collection and use. Elective choices let you deepen either mathematical theory or application-focused skills, while structured practicals and seminars ensure you gain experience implementing methods on real data—preparing you for roles in research, industry or further doctoral study.

Key modules (examples emphasized in the curriculum)

• Mathematical foundations for data science (theory behind algorithms and inference)
• Data acquisition and preprocessing methods
• Mathematical modelling of data-driven problems
• Classification techniques (statistical and algorithmic)
• Hands-on practical courses implementing methods on real datasets
• Seminars on current research topics and applied projects
• Courses or electives addressing social and ethical dimensions of data handling
• Independent research project and Master's thesis (final semester)

Learning outcomes

• Mastery of the mathematical principles that underpin modern data science approaches
• Practical skills in acquiring, preprocessing and modelling real-world data
• Ability to design and analyse classification and inference procedures with mathematical rigor
• Experience conducting independent research and communicating scientific results
• Awareness of ethical, legal and societal issues related to data collection and usage
• Capacity to customise study focus toward theoretical, applied or interdisciplinary directions

Programme requirements (structure and components)

• Duration: 2 years / 4 semesters
• Combination of lectures, practical courses and seminars
• Core technical coursework plus elective options for specialization
• Integration of social/ethical topic(s) within the curriculum and electives
• Independent research culminating in a Master's thesis in semester 4

Graduates are prepared for technically demanding roles that require deep mathematical and algorithmic expertise. Typical employment paths include research and development positions in machine learning and AI, quantitative roles in industry (data scientist, machine learning engineer, quantitative analyst), and positions in data-driven product development or consulting. The programme also provides a solid foundation for doctoral studies and academic research in mathematics, statistics, and data science.

Because of the strong theoretical grounding combined with practical project experience, alumni are competitive for roles in tech companies, research labs, finance and engineering firms as well as public-sector organisations concerned with data analysis and policy. The interdisciplinary elements also equip graduates to engage with legal, ethical and societal aspects of data work.