Data Analytics (MSc)

Overview

This international, English-taught MSc programme delivers a broad and modern education in data science by combining rigorous coursework in machine learning, big data technologies and statistical methods with application-focused content from selected domains. You will develop both the theoretical foundations and hands-on skills needed to model, analyse and interpret complex systems, with teaching closely linked to ongoing research and industry practice. Courses are run by experienced faculty and domain experts to ensure a balance of theory and real-world application.

The degree prepares graduates to tackle practical problems across a range of sectors — for example marketing, finance and logistics, as well as scientific areas like computer science and environmental studies. A major feature is an extended, supervised research or development project that spans one academic year, giving students the opportunity to explore advanced machine-learning topics, gain practical research experience and often present work at major conferences.

Core requirements / programme components

• Rigorous modules in state-of-the-art machine learning, big data technologies and statistical analysis
• Application modules that explore machine-learning use cases in domains such as natural language processing, business administration, psychology and climate science
• Methodological specialisation through advanced courses (examples include computer vision, time series analysis and other cutting‑edge methods)
• A one-year supervised project under a faculty member, focused on advanced ML topics and offering hands-on research experience; many student projects have been presented at conferences and received awards
• Strong integration of teaching with active research and industry practice, taught by experienced faculty and domain experts

This two-year (four-semester) Master of Science in Data Analytics totals 120 credit points (CP) and is organized into three parts: a strong methodological core (about 65% of the programme), an application area (≈10%), and an independent Master's thesis (≈25%). The curriculum mixes lectures, hands-on lab courses, thematic seminars, team projects and a substantial research thesis, so you gain both theoretical foundations and practical experience with real datasets and systems.

Key modules build expertise in machine learning, big-data processing, optimization and control, and privacy-aware data handling. Core teaching units include Machine Learning, Advanced Machine Learning, Big Data Analytics, Modern Optimisation Techniques, Planning and Optimal Control, and Data & Privacy Protection. Practical competence is developed in lab courses (Programming Machine Learning; Distributed Data Analytics), recurring Data Analytics seminars (I–III), and two project phases that culminate in the Master's thesis (30 CP in the typical winter-track final semester, or split 6+24 CP in the summer-track). Learning outcomes focus on: designing and implementing data-analytic pipelines, applying advanced statistical and optimisation methods, engineering scalable distributed solutions, ensuring data privacy and compliance, and conducting independent research or applied projects.

The programme offers two intake schedules (winter and summer) with slightly different sequencing of the same core elements. You also choose methodological specialisation lectures and application modules to tailor the degree to particular domains. A list of available methodological specialisations and application modules is provided on the programme website. An Advanced Track is available for students who already covered the core compulsory modules at Bachelor level; those students may replace repeated core courses with additional methodological specialisation courses to deepen their expertise.

Programme requirements and core modules (CP = credit points)

• Overall: 120 CP over 4 semesters — methodological core ~65%, application area ~10%, Master's thesis ~25%

Winter intake

• Semester 1:
  • Machine Learning (Lecture) — 6 CP
  • Modern Optimisation Techniques (Lecture) — 6 CP
  • Programming Machine Learning (Lab Course) — 6 CP
  • Data Analytics I (Seminar) — 4 CP
  • Application Module (elective) — 6 CP
• Semester 2:
  • Big Data Analytics (Lecture) — 6 CP
  • Advanced Machine Learning (Lecture) — 6 CP
  • Data and Privacy Protection (Lecture) — 3 CP
  • Distributed Data Analytics (Lab Course) — 6 CP
  • Data Analytics II (Seminar) — 4 CP
  • Project (part I) — 6 CP
• Semester 3:
  • Planning and Optimal Control (Lecture) — 6 CP
  • Project (part II) — 9 CP
  • Data Analytics III (Seminar) — 4 CP
  • Methodological Specialisation (Lecture, elective) — 6 CP
  • Application Module (elective) — 6 CP
• Semester 4:
  • Master's thesis — 30 CP

Summer intake

• Semester 1:
  • Big Data Analytics (Lecture) — 6 CP
  • Data and Privacy Protection (Lecture) — 3 CP
  • Distributed Data Analytics (Lab Course) — 6 CP
  • Data Analytics I (Seminar) — 4 CP
  • Methodological Specialisation (Lecture, elective) — 6 CP
  • Application Module I (elective) — 6 CP
• Semester 2:
  • Machine Learning (Lecture) — 6 CP
  • Modern Optimisation Techniques (Lecture) — 6 CP
  • Programming Machine Learning (Lab Course) — 6 CP
  • Data Analytics II (Seminar) — 4 CP
  • Planning and Optimal Control (Lecture) — 6 CP
• Semester 3:
  • Advanced Machine Learning (Lecture) — 6 CP
  • Data and Privacy Protection (Lecture) — 3 CP
  • Data Analytics III (Seminar) — 4 CP
  • Project (part I) — 9 CP
  • Application Module II (elective) — 6 CP
  • Master's thesis (part I) — 6 CP
• Semester 4:
  • Project (part II) — 6 CP
  • Master's thesis (part II) — 24 CP

Advanced Track

• For students who have already completed equivalent coursework for the compulsory core during their Bachelor studies. Core modules need not be repeated; instead students take additional methodological specialisation courses to broaden and deepen their data-analytics expertise.

Graduates are prepared for technical and analytical roles in industry—such as data scientist, machine learning engineer, analytics consultant or big-data specialist—in sectors including marketing, finance, logistics and environmental services. The mix of practical labs, large-scale data handling and privacy training supports implementation roles that require both engineering and analytical judgement.

The programme also equips students for research careers or doctoral study in computer science and related fields, thanks to the research-oriented project and thesis components. Interdisciplinary application modules make it straightforward to move into domain-focused analytics roles (e.g., NLP research, climate-data analysis or business analytics).