Data Science

Overview The Master’s in Data Science delivers a rigorous, interdisciplinary education drawing on core methods from Computer Science and Mathematics. Taught in English, the programme emphasizes a firm grounding in the mathematical principles that underpin modern data-driven methods so graduates can critically evaluate the strengths and limitations of algorithms and models.

Curriculum focus Students study a range of topics central to contemporary data science, including machine learning, artificial intelligence and deep learning; statistical methods, matrix algorithms and the mathematical foundations of ML; data structures, algorithms and optimisation techniques for large-scale data; and data engineering topics such as big data infrastructures and data wrangling. Compulsory courses in both mathematics and computer science ensure a balanced and thorough training.

Applications and opportunities The programme is inherently interdisciplinary and collaborates closely with neighbouring departments—Medicine, Economics, Engineering, Geography, Material Science, Physics and others—offering many practical application areas. It also maintains strong links to industry and research, positioning graduates to contribute to digitalisation efforts and the effective use of data across sectors.

Entry expectations

• A solid background in mathematics (e.g., probability, linear algebra, calculus)
• A strong foundation in computer science (e.g., algorithms, data structures, programming)
• Readiness to engage in interdisciplinary projects and to work with large-scale data and engineering tools

Overview

This master’s curriculum builds a solid technical and theoretical foundation in data science while giving you freedom to specialise in advanced topics. The programme begins with a 32‑credit core that covers algorithms and data engineering, machine learning, and the necessary mathematical background. These modules ensure you can design scalable data pipelines, implement learning algorithms, and understand the formal principles that underpin modern data analysis.

Specialisation and research

At least 36 credits are reserved for advanced or elective courses, allowing you to tailor the degree to your interests—options span areas such as quantum algorithms, statistics, optimisation, modelling and deep learning. Practical and research skills are developed through a 10‑credit Data Science Project and seminars (one in mathematics and one in computer science, 4 credits each), where you practice scientific methods, technical communication and teamwork. A dedicated 4‑credit course on social aspects and ethics of data science ensures you learn to consider privacy, fairness and societal impacts in your work.

Master’s thesis and outcomes

The programme concludes with a 30‑credit master’s thesis that synthesises your knowledge in an extended research or application project. Graduates will be able to integrate mathematical reasoning, algorithmic design and machine learning techniques, carry out independent scientific work, communicate results to technical and non‑technical audiences, and address ethical implications of data‑driven systems. The listed components sum to at least 120 credits for the complete degree.

Program requirements (concise)

• Core Data Science Methods: 32 credits (includes Algorithms & Data Engineering, Machine Learning, Mathematical Foundations)
• Advanced Data Science Methods / Electives: at least 36 credits (examples: Quantum Algorithms, Statistics, Optimisation, Modelling, Deep Learning)
• Data Science Project: 10 credits
• Seminars: Mathematics (4 credits) and Computer Science (4 credits)
• Social Aspects of Data Science / Ethics: 4 credits
• Master’s Thesis: 30 credits
• Total minimum programme load: 120 credits

Graduates are prepared for technical and research-oriented roles such as Data Scientist, Machine Learning Engineer, Data Engineer or Research Assistant in academia and industry. The programme’s mix of mathematical rigour, algorithmic training and practical data-engineering skills suits careers in sectors like healthcare, finance, engineering, materials science, geosciences and technology companies.

The curriculum also provides a solid foundation for doctoral studies in computer science, mathematics or data-centric research fields, thanks to its focus on scientific project work and a substantial master's thesis.