Data science combines mathematics, statistics, computer science, and engineering to extract knowledge from data, support evidence based decision making, and build adaptive systems that learn from experience. Over the past decade it has become indispensable in domains as diverse as pharmaceutical research and development, medical imaging, computational social science, digital humanities, and public policy.
Today’s wave of «generative AI»—especially large language models and diffusion models—has amplified that impact: LLM (=Large Language Model) powered assistants summarise medical literature in seconds, protein folding networks accelerate drug discovery, and foundation model surrogates speed up climate simulations. These advances illustrate the «unreasonable effectiveness» of data driven methods and are reshaping legal frameworks, public debate, and the very ways we work, think, and communicate.
A typical data science workflow starts with exploratory analysis and data cleaning, progresses through modelling and scalable computation, and culminates in clear visualisation and communication of new knowledge. Because of its sweeping importance, data science is often called the «fourth pillar» of scientific inquiry, standing alongside theory, experiment, and computational science.
Focal area of teaching and research
The MSc in Data Science at the University of Basel offers a rigorous foundation in mathematics, statistics, and machine learning together with a full repertoire of modern systems courses for secure, efficient handling of large scale data.
Key features
Integrated AI & LLM track – new modules on AI, generative modelling, and foundation models complement the existing machine learning core.
AI for Science electives – choose from courses ranging from computational biology to psychology that demonstrate how data science drives discovery.
Scalable systems training – hands on work with distributed databases, clusters, and GPU accelerators housed in sciCORE—University of Basel’s high-performance computing facility.
Research immersion – through the University’s Center for Data Analytics you can write your Master’s thesis on real world data drawn from life sciences, fintech, humanities, or physics, supervised by internationally recognised faculty.
The two year, 120 ECTS programme culminates in the degree Master of Science in Data Science, and provides an ideal springboard to PhD study in Data Science, Computer Science, or Mathematics, or to leadership roles in academia.
Course structure
The Master of Science degree is the postgraduate degree after the Bachelor's programme. The program awards 120 ECTS credits in total. The Master’s degree program Data Science is a so called mono-course consisting of only one core subject. One ECTS credit point roughly equals 30 hours of studying.
Master of Science (120 ECTS)
Data Science
120 ECTS
Mono-courses
Combination of subjects
The degree programs at the Faculty of Science are generally mono-courses.
Career opportunities
With an MSc in Data Science—and, if desired, a subsequent doctorate—you are exceptionally well positioned for high impact roles such as
AI research scientist in pharma, biotech, or chemicals
LLM product engineer in fintech, e commerce, or media
Data platform architect for health tech, automotive, or smart manufacturing companies
Quantitative analyst in banking, insurance, or hedge fund management
Policy advisor using data driven evidence in government or NGOs
Demand for experts who can marry rigorous fundamentals with generative AI know how far outstrips supply, making data scientists among the most sought after and best paid professionals worldwide.
Academic advice & Student Advice Center
Academic advice
Academic advice provided by faculty members can help with questions regarding the course content and structure of the specific subject/course (e.g. study and examination regulations, timetables, course structure, combination of subjects, working techniques, exam preparation).
The Student Advice Center provides support with general questions about studying, choosing a course of study and career planning, as well as orientation and decision-making conflicts or questions about combining and changing courses.
Are you interested in current scientific developments and research findings in this field? On the research websites, you will find numerous publications by researchers from the University of Basel. This resource offers valuable insights and helps you get to know a subject area better–both as an orientation aid and as an accompanying source of information during your studies.
