Data Science

Overview

Data science has evolved into a distinct discipline that sits at the intersection of computer science, mathematics and domain-specific applications. This English-taught Master’s program combines rigorous mathematical foundations with core computer-science techniques for data processing, analysis and management, and offers a range of applied fields across the university. Because of this mix of theory and practice—and the high demand for skilled practitioners—graduates are well positioned for roles in industry, technology and research.

Program structure and learning approach

The curriculum is built around three pillars: mathematical foundations for data analysis, computer-science methods for handling and analysing data, and an application area where these methods are applied to real-world problems. To accommodate students from diverse academic backgrounds, the program offers ramp-up courses in mathematics and computer science so all participants reach a comparable starting level. Students can shape their own profile through a broad choice of modules across the three pillars; seminars, labs and project work emphasize hands-on, research-oriented learning and the direct application of theoretical methods.

Applied focus and research component

The programme draws on expertise across faculties, so application tracks span engineering, biology/chemistry/pharmacy, medicine, image and signal processing, with additional application areas under development. The Master’s thesis—an independent scientific research project—is completed within a six-month period under supervision in one of the computer science or mathematics institutes and is typically linked to an application area. The combined offering is distinctive: the university has a long tradition in computer science (since 1972) and mathematics, and since 2018 has provided a mathematics specialization in data science—making this Master’s a unique blend of mathematical depth, computational skill and interdisciplinary application.

Key facts / requirements

• Language of instruction: English
• Jointly offered by the Departments of Computer Science and Mathematics, with participation from other faculties
• Three core areas: mathematics (foundations for data analysis), computer science (data processing, analysis, management), and an application area
• Ramp-up courses available to align students with different prior degrees/backgrounds
• Strong emphasis on seminars, labs and project-based, research-oriented learning
• Master’s thesis: independent scientific research completed within a six-month period, supervised by a computer science or mathematics institute, usually connected to an application area
• Example application areas: engineering; biology/chemistry/pharmacy; medicine; image and signal processing (additional areas being prepared)

Curriculum overview

This Master's in Data Science is built from several focused parts that together develop advanced technical skills, domain-applicable experience, and professional competencies. The programme begins with a 10-credit ramp-up phase to ensure all students share a common foundation. Students then choose from elective modules in computational methods, mathematical foundations, applied data science, and professional/key-ethical topics, and complete a research-oriented master's thesis with a presentation.

Key modules emphasize:

• Computational methods and concepts (25 credit points): advanced algorithms, programming and computational techniques for large-scale data processing.
• Mathematical methods and concepts (25 credit points): rigorous foundations in statistics, optimization, and theory that underpin modern data science.
• Data science in applications (15–20 credit points): applied projects or domain-specific modules that train you to translate methods into real-world solutions.
• Key qualifications and ethics (10–15 credit points): professional skills, communication, and the ethical, legal and social aspects of data use.
• Master's thesis and presentation (30 credit points): an independent research or project-based capstone demonstrating your ability to carry out a substantial data-science investigation.

Students work with an assigned tutor to design an individual study plan at the start of the programme so they can set personal study priorities and align elective choices with career or research goals. For full module descriptions, assessment methods and scheduling details, consult the module guide: https://www.tu-braunschweig.de/en/data-science/documentsPDF

Requirements (concise)

• Ramp-up phase: 10 credit points
• Compulsory electives — Methods and Concepts of Computational Sciences: 25 credit points
• Compulsory electives — Methods and Concepts of Mathematics: 25 credit points
• Compulsory electives — Data Science in Applications: 15–20 credit points
• Compulsory electives — Key Qualifications and Ethics: 10–15 credit points
• Master's thesis including presentation: 30 credit points
• Individual study plan developed with a tutor at programme start

(Taken together, these components amount to approximately 115–125 credit points depending on elective selections.)

Graduates are prepared for data-focused roles in industry, technology and research. Typical positions include data scientist, machine learning engineer, data analyst, and research scientist; employers span sectors such as engineering firms, pharmaceutical and biotech companies, healthcare, imaging and signal-processing companies, and technology consultancies.

The programme also provides a solid foundation for doctoral studies for students interested in academic research. The strong combination of mathematical theory, computational methods and application experience makes alumni attractive candidates for both applied and research-driven positions.