MSc Biostatistics and Data Science
The master's programme in Biostatistics and Data Science combines statistics and data science to address challenges in biology, medicine, and public health. It is designed for students with backgrounds in Mathematics, Statistics, Computer Science, or related disciplines. The programme is offered jointly by KTH, Karolinska Institutet, and Stockholm University through the Stockholm Trio university alliance.
Biostatistics and Data Science at KTH, Karolinska Institutet and Stockholm University
In this two-year master's programme, you will develop the key skills required to work as a biostatistician or data scientist. The programme combines statistics, computational science, and programming with theoretical and practical training to address challenges in biology, medicine, and public health. You will also learn the analytic techniques used in data science to prepare you for the data-driven challenges of modern medical research and a career as a data scientist. During your two years in Stockholm, you will study a tailored selection of courses from three universities with prominent research in their respective fields: Karolinska Institutet, KTH Royal Institute of Technology, and Stockholm University.
The programme introduces you to probability theory, statistical inference, statistical modelling of biomedical data, and computer-intensive methods in mathematical statistics. You will receive an introduction to human biology, physiology, genetics, and medical research, and gain an understanding of the multidisciplinary nature and roles of biostatistics and data science in biology, medicine, public health, and society. As the programme progresses, you explore topics such as machine learning, statistical learning, Bayesian inference, missing and correlated data, and methods for designing and analysing pre-clinical studies, clinical trials, and observational studies.
Programme structure and progression
During the first year, you establish a foundation in mathematical statistics and biostatistics while progressing to elective courses in areas such as machine learning and AI, mathematical statistics, and biostatistics. In the second year, you deepen your knowledge through advanced courses in biostatistics and the design and analysis of medical research studies. The programme concludes with an independent degree project in an academic, government, or industrial setting, with the possibility of carrying it out abroad.
Courses in the programme
The programme courses cover topics such as biostatistics, data science, machine learning, artificial intelligence, epidemiology, programming, statistical methods, mathematics, and medical science.
Courses in the master's programme in Biostatistics and Data Science
Future and career
The combination of biostatistics and data science gives graduates an excellent profile for challenging and rewarding careers in industry (for example, biomedical, healthcare, insurance, and pharmaceutical sectors), government (for example, public health agencies) and academia. There is a shortage of trained biostatisticians and data science professionals, both in Sweden and internationally. After graduating from this programme, you will also find opportunities for doctoral studies, both in developing new biostatistics and data science methods and applying your knowledge and skills in biostatistics and data science to address research topics in biology, medicine, and public health. Although there is a focus on applications in life science, much of the content is generic and applicable to careers within any branch.
Sustainable Development
Graduates have the knowledge and tools for moving society in a more sustainable direction, as sustainable development is an integral part of all programmes. The three key Sustainable Development Goals addressed by the master's programme in Biostatistics and Data Science are Goals 3, 4, and 10: Good Health and Well-being, Quality Education, and Reduced Inequalities.
Goal 3 is central to the programme, as it trains experts who support medical research and life sciences, ultimately advancing global health. Through a Stockholm Trio-framed collaboration—uniting the strengths of KI, KTH, and SU—Goal 4 is advanced via inclusive, high-quality education of international calibre. Lastly, Goal 10 is supported by promoting data-driven research to inform equitable, evidence-based policymaking.
During their training in the programme, students develop strong analytical and computational skills, with a focus on real-world challenges in health and life sciences. They learn to work across disciplines and use data responsibly to support evidence-based solutions. After graduation, they apply these skills in roles such as biostatisticians or data scientists, contributing to improved public health, reduced inequalities, and more sustainable, data-driven decision-making.
Faculty and research
All compulsory KI courses in the programme are organised by the Department of Medical Epidemiology and Biostatistics (MEB) . As Sweden's leading medical university, KI combines research in statistical methodology with applications in biology and medicine. While courses across all three universities cover theory, methods, and applications, those at KI place greater emphasis on applications. Teaching is led by biostatisticians and complemented by guest lecturers from fields such as clinical medicine, epidemiology, and genetics.
At KTH, courses in the programme are taught by teachers and active researchers with expertise in mathematical statistics, machine learning, and data science, mainly from the Departments of Mathematics and Intelligent Systems. Students in the programme often take courses alongside those in KTH’s master’s programmes in Applied and Computational Mathematics and Machine Learning, creating a stimulating environment with peers from complementary fields.
At Stockholm University, courses are taught by active researchers in the Department of Mathematics. Depending on the course, you study statistical methodology, machine learning, and probability, often applied to biological or medical data. Many courses are shared with students from master's programmes in Mathematical Statistics, Computational Mathematics, and Mathematics, providing opportunities to study alongside students with complementary backgrounds.
The three universities contributing to this programme are partners in a research and education through the Stockholm Trio university alliance and collaborate on research infrastructure such as the Science for Life Laboratory .
