This programme trains graduates with strong mathematical and analytical skills to become impactful practitioners in applied artificial intelligence and data science. It emphasizes turning quantitative theory into practical insight so you can contribute meaningfully to data-driven decision making. The goal is to produce professionals who not only understand algorithms and models but can also communicate findings and influence business outcomes.
Built on rigorous quantitative foundations, the curriculum moves from core principles to applied methods, giving you a comprehensive grasp of how data science solves real-world business problems. Teaching is delivered in English and the programme is housed in Frankfurt am Main, a major European financial and business hub—providing a context that strongly connects technical skills with industry-relevant applications.
Requirements (typical applicant profile)
This Master’s programme is built around four complementary pillars that together provide a broad, applied education in data science and AI. The curriculum begins with a deep technical foundation—covering the theory and tools behind data science, from algorithms and data structures to computational statistics, machine learning, deep learning and cloud computing. A second strand connects these technical skills to business processes, showing how data-driven methods improve operations and inform strategic decisions. The third pillar addresses the ethical and legal issues that surround AI and statistical technologies, equipping students to recognise and manage risks and responsibilities. The final pillar emphasises translating knowledge into practice by developing data-driven solutions that support innovation and measurable business impact.
Key modules give both conceptual depth and hands-on experience: algorithms & data structures, computational statistics, machine learning, deep learning, and cloud computing are central. Learning outcomes include technical proficiency in modern data-science toolkits, the ability to integrate analytical results into business workflows, critical understanding of ethical and regulatory constraints, and practical skills to design and implement AI-driven solutions that drive organisational value.
To support students who need a quick refresher, free preparatory courses in Python and Mathematics are offered in August before the programme starts—ideal for international students who want to arrive ready to engage with the core curriculum.
Requirements / preparation (concise)
Applicants must meet the following core admission criteria. You need a completed first university degree (Bachelor’s or Diploma) with at least 180 ECTS credits or an equivalent qualification from your country. If your degree was awarded under a different credit or degree system, you should be prepared to demonstrate equivalence during the application process.
You must also demonstrate an excellent command of written and spoken English — the programme lists accepted proofs and minimum scores on its information page, so check those details when preparing your application. In addition, the programme requires either a valid GMAT or GRE test score, or completion of the Frankfurt School Admission Test (BT Methods). Finally, admission is contingent on passing a formal interview as part of the selection process.
Admission requirements (summary)
Winter Semester (International)
30 June 2026
Winter Semester (EU/EEA)
30 June 2026
Graduates are prepared for technical and applied roles such as data scientist, machine learning engineer, AI consultant, data analyst or business intelligence specialist across industries (finance, consultancy, technology, fintech and corporate sectors). The programme’s combination of rigorous quantitative training and business process integration equips alumni to design, implement and communicate data-driven solutions that influence decision-making and operational strategy.
The ethical and legal training further positions graduates for roles that require responsible AI deployment and compliance awareness. With a strong technical foundation, some graduates may also pursue research or doctoral studies in data science or related computational fields, while others will move directly into industry roles at startups, consultancies, or large enterprises seeking applied AI expertise.
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