This Master's programme provides advanced knowledge and practical skills in data science, with an engineering emphasis on both data and knowledge. It is designed to help motivated Bachelor's graduates transform passive data into usable knowledge by teaching the methods and systems needed to represent, manage and extract value from large and diverse information sources. The degree is awarded as a Master of Science.
The curriculum covers the representation, management and interpretation of data and knowledge assets. Topics include the design and development of advanced databases, knowledge bases and expert systems; techniques for extracting models and patterns from structured data, text and multimedia; and modelling tools for representing and updating the knowledge discovered. Instruction is offered in either German or English, so the programme is accessible to students who are proficient in one of these languages.
Graduates are prepared for roles across business intelligence, industrial applications, life sciences and biotechnology, as well as security-related fields. Managing and analysing data and maintaining organisational knowledge assets are crucial skills sought after by companies, government agencies and non-profit organisations.
Requirements
Curriculum overview
The programme is organised into four thematic areas that together build a comprehensive Data Science education. Students must earn ECTS credits in each area: Fundamentals of Data Science, Learning Methods and Models, Data Processing and Data Science, and Applied Data Science. Study is arranged over four semesters, with coursework and at least one team project completed in the first three semesters and the Master’s thesis written in the fourth semester.
Key modules and learning outcomes
Graduates will be able to combine solid theoretical foundations with practical skills: formulating data problems, selecting and implementing appropriate models and processing workflows, and applying solutions in domain-specific settings. Hands-on team projects (including at least one 6-ECTS team project in Applied Data Science) develop collaboration, communication and applied problem-solving abilities, while the fourth-semester Master’s thesis demonstrates capacity for independent research or an advanced applied project.
Program requirements (concise)
Prospective international students should consult the university webpages for up-to-date information on admissions and the application process. The general page covers overall admission rules and how to apply as an international applicant, while the programme-specific page lists the exact entry requirements and application deadlines for this master’s degree. The programme’s course website provides additional details about the curriculum and contact information for the department.
Make sure to review these pages well before application deadlines so you can gather required documents, confirm language and academic eligibility, and follow the prescribed application steps. If anything is unclear, use the contact details on the programme or university pages to reach admissions or the programme coordinator.
Winter Semester (International)
15 May 2026
Summer Semester (International)
15 November 2026
Graduates are prepared for technical and interdisciplinary roles that require both data engineering and knowledge engineering skills. Typical pathways include positions such as data scientist, knowledge engineer, database/AI system developer, or analytics specialist in industry, healthcare, biotech, security, government agencies and non-profit organisations.
The programme’s mix of modelling, systems design and applied projects also provides a solid foundation for research or doctoral studies in data science, artificial intelligence and information systems.
Hochschule für Technik Stuttgart - University of Applied Sciences — Stuttgart
University of Regensburg — Regensburg
University of Bonn — Bonn
Brandenburg University of Technology Cottbus-Senftenberg — Cottbus