Overview This English‑taught MSc program in Data Science is an interdisciplinary course designed to let graduates from diverse academic backgrounds (natural sciences, engineering, social sciences, etc.) build a specialist profile in data science. The curriculum balances mathematical and statistical foundations with hands‑on tools and modern computing environments, preparing students to work with complex datasets and to design data‑driven solutions.
Structure and curriculum The course is organised across three semesters. The first semester establishes core knowledge (with an optional pre‑course in programming) through modules such as Tools and Programming Languages for Data Science, Mathematics and Multivariate Statistics, Data Visualisation and Visual Analytics, Machine Learning, Data Management and Cloud Computing. In the second semester students move into advanced topics—Social Media Analytics, Big Data Technologies and Deep Learning—alongside an application project and an elective module to deepen a chosen focus. The third semester is devoted to an independent Master’s thesis and a concluding colloquium.
What you will gain Graduates acquire practical and theoretical competencies in programming for data science, statistical modelling, machine learning, visual analytics, cloud and big‑data technologies, and applied deep learning. The mix of taught modules, a project, and the thesis gives experience in turning data insights into deployable solutions—skills relevant for roles in industry, public sector analytics and research.
Admission considerations (concise)
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Applicants should hold a relevant Bachelor’s degree with a minimum final grade of 2.5 (German grading scale). You must demonstrate university-level coursework in both Mathematics/Statistics and Computer Science: together these must total at least 30 ECTS, with a minimum of 10 ECTS in Mathematics/Statistics and 10 ECTS in Computer Science.
If your prior degree awarded fewer than 210 ECTS but at least 180 ECTS, you will be required to make up any missing competencies before or during the Master’s programme. As a general expectation, prior studies should amount to 300 ECTS in total. At the start of the programme the examination board will specify which competencies you must complete and the latest deadline for providing proof of those achievements.
English language proficiency is required (see the programme’s language requirements for accepted certificates and minimum scores). Applicants whose Bachelor’s degree was obtained outside the Bologna Area / European Higher Education Area must also submit GRE results; a minimum score of 65% is required in each GRE section (Verbal Reasoning, Quantitative Reasoning and Analytical Writing).
Admission requirements — key points
Winter Semester (International)
https://www.haw-kiel.de/en/degree-courses/information-for-prospective-students/courses-and-application/
Graduates are prepared for technical and analytical roles that require advanced data science skills, such as Data Scientist, Machine Learning Engineer, Data Analyst, Big Data Engineer or AI Specialist. The curriculum’s emphasis on machine learning, deep learning, data management, cloud computing, visual analytics and application projects equips students to handle end-to-end data workflows in industry, startups, consulting and the public sector.
Hochschule für Technik Stuttgart - University of Applied Sciences — Stuttgart
University of Regensburg — Regensburg
University of Bonn — Bonn
Brandenburg University of Technology Cottbus-Senftenberg — Cottbus