This English-taught Master's programme blends the technical foundations of data science and AI with the business-facing skills needed to work as an applied data scientist. Coursework covers core statistical and computational topics while also training you to frame problems creatively, communicate findings, and consider ethical and legal implications in an international context.
On the technical side, the programme includes modules on big data architectures, data and text mining algorithms, neural networks, and predictive analytics. Complementing those topics are courses in creative questioning techniques, visualization and data storytelling, and aspects of ethics and international law—preparing you to apply models responsibly and to present results to non-technical stakeholders.
Theory is reinforced through a sequence of three case studies that grow in complexity over the first three semesters, giving you hands-on experience with real-world problems. The fourth semester is reserved for the Master’s thesis, which students are encouraged to align with industry-relevant topics when possible.
Program structure and teaching method The degree is delivered in theme-centred five-week teaching blocks, a compact format that concentrates on a single topic at a time. Each block combines classroom-based modules with a connected case study so that theory and practice are taught side-by-side. Case studies grow in scale and complexity from semester to semester, giving students repeated opportunities to apply newly learned methods to realistic problems.
Key modules and progression In the first semester students complete the core foundational modules that teach the essential knowledge and methods needed for later work. The second and third semesters are organised as self-contained units that deepen and broaden technical skills; every semester includes at least one substantial case study linked directly to the technical coursework. The programme culminates in a Master’s project that serves as the most comprehensive case study, integrating material from earlier blocks.
Learning outcomes and practical focus By repeatedly working on progressively more complex case studies—including the Master’s project—students learn how the different phases of large-data and AI projects interact (from data collection and preprocessing, through modelling and evaluation, to interpretation and deployment). The block structure and tight coupling of theory to hands-on cases are designed to build applied competencies, project experience, and the ability to manage and communicate solutions across stages of a big-data/AI workflow.
Program requirements (curriculum-related)
You must hold a first professional university degree in one of the specified subject areas and the degree must comprise 180 credit points (CPs). This requirement applies to degrees in closely related technical and quantitative fields.
If your bachelor’s qualification comes from a different credit system or from an international institution, check with the admissions office for how your credits and degree title are assessed for equivalence.
Winter Semester (International)
There areno application deadlines,so you can apply at any time until all of the places in the programme have been filled.
Graduates are prepared for roles such as applied data scientist, machine learning engineer, AI analyst, or data engineer in industry, consulting or research settings. The combination of technical expertise and applied case work enables rapid contribution to real-world projects involving big data architectures, predictive modelling and production-ready analytics.
The programme’s emphasis on communication, visualisation and ethics also positions alumni for cross-functional roles where translation between technical teams and business stakeholders is required, as well as for international employment given the English-language delivery and exposure to global legal and ethical frameworks.
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