This master's program trains you to turn raw data into actionable knowledge. You will learn to work with very large, heterogeneous, and sometimes unreliable data sets, developing techniques to clean, analyze and interpret information so it can be used in real-world decision making. The subject draws on computer science, mathematics and statistics and is closely connected to many practical application areas.
The curriculum balances data analysis and systems engineering: you will study modern statistical and machine learning methods for extracting insights as well as algorithms and software techniques for building information systems that process and serve data. The result is a blend of analytical skills and engineering know‑how that prepares you to both extract value from complex data and to design the systems that enable that work in research or industry. The program is taught in English in Aachen, Germany, making it accessible to international students.
Key preparations and requirements
The program runs over four semesters (two academic years) and is designed to deliver a balanced mix of theory, methodological training, and hands-on practice. Students typically complete roughly 30 ECTS credits each semester through a combination of lectures across different areas, seminars, practical lab work, and a research-focused Master's thesis in the fourth semester.
At the curriculum’s core is a foundational area made up of basic and advanced courses on the essential subjects of data science. This foundation is paired with a specialization track: you may focus further within computer science or mathematics, or choose an application domain such as business analytics, computational life science, computational social science, or physics. Teaching formats include formal courses for core knowledge, seminars for critical discussion and presentation skills, and labs for practical implementation and experimentation.
Graduates will emerge with both broad and deep competencies: solid theoretical grounding in core data-science concepts, advanced methodological skills in the chosen specialization, practical experience from lab work and seminars, and the ability to carry out independent research documented in the Master’s thesis. For international students, the structure supports a clear workload rhythm (about 30 ECTS per semester) and flexibility to tailor the degree toward either research or applied career paths in industry or academia.
Key facts and requirements
Applicants must hold a bachelor’s degree in computer science, mathematics, physics or a closely related field. In addition, you must meet one of the program profiles (computer science, mathematics or physics); detailed descriptions of these profiles are available on the program’s information pages. Your prior coursework and ECTS credits will be checked to ensure they align with the program’s expectations.
If you do not have EU citizenship or do not hold a German Abitur, you are required to submit GRE scores. Module descriptions and ECTS information used for comparability checks can be found in the electronic lecture directory (RWTHonline); the content and credit structure of your previous modules must be comparable to RWTH’s corresponding modules.
Required admission items (concise)
Winter Semester (International)
1 March 2026
Summer Semester (International)
1 September 2026
Winter Semester (EU/EEA)
15 July 2026
Summer Semester (EU/EEA)
15 January 2027
Graduates will be prepared for roles that require strong quantitative, algorithmic and systems skills, such as data scientist, machine learning engineer, data analyst, and software engineer for data-driven systems. The programme’s combination of data analysis methods and systems engineering equips students to build, deploy and evaluate large-scale data processing and machine learning systems in industry and research.
Career paths span sectors including business analytics, biotechnology and computational life sciences, social science research, physics and engineering, and technology companies. The solid methodological foundation also enables graduates to pursue doctoral studies and research careers in data science and related disciplines.
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