Master of Science in Data Science

Program overview

This two-year, English-taught Master's in Data Science is an interdisciplinary degree run jointly by the Departments of Statistics, Computer Science and Mathematics. The programme — which can be started in either the winter or summer semester — builds directly on the department’s six-semester Bachelor’s in Data Science and focuses on current methods and rigorous mathematical and statistical foundations.

What you will study and gain

The curriculum emphasizes advanced statistics and the development of underlying statistical theory, while also covering contemporary computational and methodological approaches from computer science and mathematics. Students are trained to conduct independent scientific research, to use methods responsibly, and to communicate across disciplinary boundaries and application domains — a key aim is fluency at the interface of statistics, computer science, mathematics and real-world applications.

Structure and expectations

Unlike many MSc Data Science programmes, this master’s does not include a separate “review” semester revisiting basics in mathematics, statistics and computer science. That is because it assumes entrants already possess a strong, compatible background (typically the programme’s own German-language Bachelor’s). If you have gaps in foundational knowledge, you are expected to address them on your own; the benefit is that more of the curriculum is devoted to specialised, master’s-level topics and research-oriented training.

Key facts and applicant expectations

• Duration: 2 years (Master of Science)
• Language of instruction: English
• Start dates: winter or summer semester
• Jointly offered by: Departments of Statistics, Computer Science and Mathematics
• Builds on: the programme’s six-semester BSc in Data Science (taught in German) or an equivalent strong background
• Academic emphasis: advanced statistics with a strong focus on statistical theory, plus contemporary computational methods
• Structure note: no dedicated review semester for foundational topics — incoming students are expected to already have solid prior knowledge or to remedy gaps independently
• Outcome focus: preparation for scientific research, responsible use of methods, and interdisciplinary communication between theory and applications

This master's curriculum combines a structured set of core courses with a wide range of electives so you can shape the program around your interests and prior studies. Coursework builds on earlier university-level training and moves into advanced topics, giving you both breadth across data-science areas and the chance to specialise. The programme is designed to let you mix theoretical study with applied options so that your pathway can be tailored to a research or practice focus.

Key advanced modules cover statistical theory, statistical learning (machine learning and predictive modeling), and big-data methods. Practical application of these techniques is embedded through case studies, seminars and elective courses that apply methods to selected fields. A particular focus of the curriculum is on developing efficient approaches for analysing very large datasets and on translating statistical/algorithmic methods into scalable solutions.

Hands-on experience is an explicit part of the programme: internships, project-based courses and a Master’s thesis give you multiple opportunities to implement methods on real problems, validate models, and communicate results. Graduates will be expected to design and evaluate statistical and learning-based analyses, apply scalable techniques to large datasets, and adapt methods to domain-specific questions through project work and research.

Program components (requirements)

• Core modules and a broad selection of elective modules to personalise your study plan
• Advanced coursework in statistical theory, statistical learning and big-data techniques
• Application-focused offerings: case studies, seminars and elective courses in selected fields
• Practical elements: internships and project-based courses for hands-on experience
• A Master’s thesis to demonstrate independent application of methods and research skills
• Builds on prior university-level studies in a related area (students are expected to have relevant background)

Graduates are prepared for roles that require strong statistical and computational expertise, such as data scientist, statistical analyst, machine learning engineer, or research positions in industry and academia. The programme’s emphasis on statistical theory and large-scale data methods also provides a solid foundation for those who wish to pursue a PhD.

Hands-on project work, optional internships and close links between statistics, computer science and mathematics help students develop practical skills valued by employers in sectors such as technology, finance, healthcare, research institutions and public sector analytics.