This MSc trains students with a STEM background to turn complex data into actionable business value. Positioned where data analytics meets operations research, the programme combines theoretical and practical methods so you can apply data-driven approaches in real-world decision-making. Core strengths include mathematical optimisation, machine learning, heuristic algorithms, and design and simulation — a mix aimed at giving you a clear competitive edge.
The curriculum emphasizes making robust decisions in uncertain and rapidly changing environments. That capability is increasingly important: graduates from this programme find roles across a wide range of sectors because almost every industry now relies on data- and algorithm-driven decision processes. Expect to build skills that employers look for when they need people who can translate data into dependable operational and strategic choices.
For international students, note the programme is delivered in English and prepares you for cross-industry work where analytical rigor and practical implementation meet. If you enjoy working with data and want to influence business outcomes through optimisation and algorithmic methods, this degree equips you with both the technical toolbox and the decision-focused perspective employers seek.
Admissions profile / requirements (concise)
This full-time, four-semester (two-year) MSc is built to combine rigorous, method-based instruction with hands-on, practice-oriented learning. The programme trains students to become industry-independent data experts: you will learn foundational methods and statistical thinking that transfer across software tools and sectors, rather than training on a single proprietary stack. Teaching balances theory with applied case work provided by the programme’s network of business partners.
Practical experience is an integral part of the curriculum. Several modules use real case studies drawn from industry, and an internship is embedded in the programme to ensure a well-rounded mix of academic theory and workplace application. A range of elective courses lets you tailor your studies toward specific sectors and career goals so you can develop domain knowledge in addition to core data-science skills.
Key modules include Machine Learning, Heuristic Optimisation, Energy and Climate Analytics, Health Care Analytics & Management, and Management & Technology Perspectives. Together these courses equip you to extract insights from complex data, design and optimise decision-making processes, and communicate technical results to management and stakeholders.
We are seeking applicants who hold a Bachelor of Engineering or Bachelor of Science in a STEM discipline and who come from a technological background. Successful candidates will have completed focused coursework in quantitative and technical subjects and should bring at least one year of professional work experience.
You should be prepared to document your prior studies so the admissions team can verify the required credit totals and subject coverage. If your degree system uses different credit units, show a clear mapping or transcript that demonstrates equivalence.
Winter Semester (International)
1 March 2026
Winter Semester (EU/EEA)
31 August 2026
Graduates are prepared for roles that require turning data into decision-making value across industries — examples include data scientist, decision scientist, optimisation specialist, analytics consultant, supply chain and operations analyst, and roles in energy, healthcare, manufacturing, finance and logistics. The programme's focus on mathematical optimisation and algorithmic decision-making makes graduates particularly attractive for companies seeking to deploy data-driven operational solutions.
With strong industry links and a mandatory internship, alumni typically enter industry positions in large corporates and tech firms or join specialised analytics consultancies. The methodological grounding also provides a solid foundation for research-oriented careers or further doctoral studies in areas such as operations research, machine learning and applied data science.
Hochschule der Bayerischen Wirtschaft (HDBW) — München
Hof University of Applied Sciences — Hof
Stralsund University of Applied Sciences — Stralsund
Technische Universität Braunschweig — Braunschweig