Data Science

This English-taught Master of Science in Data Science (Computer Science) builds on the recent successes of artificial intelligence and machine learning to show how mathematical and statistical methods can extract meaningful patterns from large datasets. The programme emphasises how automation can reveal hidden correlations and make these insights practically useful.

You will gain in-depth knowledge of contemporary data analysis and machine learning techniques, with a balance between theoretical foundations and hands-on application. Teaching covers both the mathematical and statistical underpinnings of data science and their implementation in real-world contexts.

The curriculum has an interdisciplinary focus, linking data-driven methods with traditional model-based approaches. Special attention is given to data analytics and operations research, weather and climate research, and the mathematical foundations of data science. Graduates acquire strong methodological skills and the ability to tackle complex data problems in academic research and industrial settings.

Core focus areas and outcomes

• Modern methods of data analysis and machine learning
• Mathematical and statistical foundations of data science
• Data analytics and operations research
• Applications in weather and climate research
• The interface between data-based and model-based modelling
• Preparation for solving complex scientific and industrial data problems

Curriculum overview

This master’s curriculum builds a strong quantitative and practical foundation for data science. Early-semester core courses such as Mathematics of Data Science and Operations Research introduce the mathematical and optimization tools needed for modelling and analysis. In the first semester students also encounter Advanced Programming in Python (listed as a mandatory elective), ensuring they can implement algorithms and prepare data pipelines.

Subsequent semesters focus on machine learning principles, databases and applied experience: Principles of Data Science / Principles of Machine Learning is offered in Semester 2 or 4, Database Management and an Applied Data Science Project take place in Semester 2, and an Ethics and Law course is available as a mandatory elective in Semester 3. Students must also complete 15 ECTS of mandatory elective courses in Mathematics and/or Statistics to deepen theoretical knowledge. The programme concludes with a Master’s thesis and includes a transdisciplinary elective ("Studium Pro") to broaden skills beyond strictly technical topics.

Key learning outcomes

• Solid grounding in the mathematical and statistical foundations of data science, enabling rigorous model development and evaluation.
• Practical competence in programming (Python) and database systems for building reproducible data workflows.
• Understanding of machine learning principles and ability to apply algorithms to real-world problems through the Applied Data Science Project.
• Awareness of ethical, legal and societal implications of data-driven systems.
• Capacity to integrate domain knowledge via electives and a transdisciplinary module, culminating in an independent research-based Master’s thesis.

Program requirements (concise)

• Core mandatory modules:

  • Mathematics of Data Science (Semester 1)
  • Operations Research (Semester 1)
  • Database Management (Semester 2)
  • Applied Data Science Project (Semester 2)

• Principles of Data Science / Principles of Machine Learning — mandatory (offered in Semester 2 or Semester 4)

• Advanced Programming in Python — mandatory elective (Semester 1)

• Ethics and Law — mandatory elective (Semester 3)

• 15 ECTS of mandatory elective courses in Mathematics and/or Statistics

• Choose four elective modules from any area of Data Science, its mathematical foundations, or its applications

• One transdisciplinary elective module ("Studium Pro")

• Completion of a Master’s thesis

Graduates are prepared for technical roles that require strong quantitative and computational skills, such as data scientist, machine learning engineer, data analyst, or operations research specialist in industry and public-sector organisations. The programme’s combination of theory, applied projects and domain electives (e.g. climate research) also equips students for research positions or further academic study (PhD) focused on data-driven methods.

The English-language instruction and international orientation make graduates competitive for positions in multinational companies, research institutes and internationally active organisations.