Master of Science in Data Science

Data science connects methods and tools for extracting knowledge from large and complex data sets with practical decision-making in business, government, and research. The programme teaches the techniques used for big-data processing, data modelling, machine learning and simulation, drawing equally on computer science, mathematics and statistics. Because courses are delivered by faculty from all three areas—rather than focusing on just one or two—graduates gain a broad, interdisciplinary skill set that helps them tackle problems from multiple perspectives and adapt quickly to changing professional demands.

The curriculum begins with propaedeutic (bridging) courses in the first semester so students from diverse academic backgrounds can follow the programme successfully. Modern e‑learning approaches such as flipped classrooms and electronic tutorials are used to increase engagement and give learners more flexibility in when and how they study. A compulsory small research project lets students apply theoretical knowledge to real‑world problems using scientific methods while also developing teamwork, communication and other transferable skills.

Students can personalise their studies through a wide range of elective courses—including options in economics and environmental sciences—so the degree can be aligned with specific career goals or sectors. There are also structured double‑degree possibilities in combination with computer science, business informatics, applied mathematics, business mathematics or applied statistics. The MSc also provides a solid basis for those who wish to pursue doctoral research in related fields.

Requirements / compulsory elements (as stated)

• First‑semester propaedeutic (bridging) courses to level differing academic backgrounds
• Participation in e‑learning formats (e.g., flipped classrooms, electronic tutorials) that supplement face‑to‑face teaching
• Completion of a compulsory small research project applying scientific methods to real‑world data problems
• Choice of electives across disciplines (e.g., economics, environmental sciences) to tailor the degree to career goals
• Option to complete a double Master’s degree with computer science, business informatics, applied mathematics, business mathematics, or applied statistics
• The programme is designed to prepare graduates for professional roles or further PhD study in related fields

This master's curriculum combines a solid technical foundation with options for deep, domain-specific study. Core (compulsory) coursework includes both introductory and advanced classes to build and extend essential skills — examples are Big Data Analytics, Data and Web Mining, Numerical Optimisation for Data Science, and Statistical Methods of Data Science — together with propaedeutic courses that prepare students for advanced topics. These modules focus on algorithmic thinking, scalable data processing, rigorous statistical reasoning, and numerical methods that are immediately applicable to real-world data problems.

Beyond the core, students choose elective modules organized into specialisation tracks to gain focused expertise. Available tracks include Algorithmic Optimisation, Applied Statistics, Data and Knowledge Systems, Financial Economics, Geoinformatics, Natural Language Processing, and Simulation Studies. Electives let you tailor your program toward research or industry roles — for example, choosing NLP and Data & Knowledge Systems for language-centered AI work, or Financial Economics and Applied Statistics for quantitative finance careers. Research case studies are built into the programme to give hands-on experience applying methods to concrete datasets and problems.

The degree culminates in an independent master's thesis, where you design and execute a substantial research or development project. Learning outcomes across the programme include the ability to model and solve complex data problems, implement and evaluate scalable algorithms and statistical methods, interpret results in applied contexts, and communicate findings clearly in written and oral form — preparing graduates for data science roles in industry or for further academic research.

Programme components (requirements)

• Compulsory modules: introductory and advanced courses (e.g., Big Data Analytics; Data and Web Mining; Numerical Optimisation for Data Science; Statistical Methods of Data Science) plus propaedeutic courses
• Elective modules within one of the specialisation tracks: Algorithmic Optimisation; Applied Statistics; Data and Knowledge Systems; Financial Economics; Geoinformatics; Natural Language Processing; Simulation Studies
• Research case studies providing applied, project-based experience
• Master's thesis involving independent research or a development project

Graduates are prepared for data-driven roles across industry, public institutions and research due to the programme's combination of computer science, mathematics and statistics. Typical positions include data scientist, machine learning engineer, quantitative analyst, data engineer, and specialist roles in fields such as finance, environmental science, geoinformatics and business analytics. The programme's applied research project and specialisation tracks help students develop practical skills valued by employers.

The MSc also provides a solid academic foundation for doctoral studies in related fields. Its multidisciplinary training and research components make graduates competitive for PhD programmes and research positions in academia and industrial R&D centres.