University of Geneva

What you'll study

The programme builds a rigorous foundation in probability and statistical theory while emphasising modern computational and applied methods. Core topics typically include probability theory and stochastic processes, statistical inference, multivariate analysis, regression and generalized linear models, time series, and Bayesian statistics. Applied and computational modules cover statistical learning and machine learning, resampling and Monte Carlo methods, high-dimensional data analysis, and software for data analysis (R, Python and specialised packages).

Students will also study elective subjects that reflect faculty strengths and local research activities, such as spatial statistics, survival analysis and biostatistics, experimental design, causal inference, and computational statistics. The curriculum combines lectures, practical computer-based classes, and project work, and culminates in a substantial master’s thesis or applied project carried out under faculty supervision or in collaboration with external partners.

Entry requirements

Applicants are expected to hold a recognised bachelor’s degree (or equivalent) in statistics, mathematics, actuarial science, economics, computer science, engineering or a closely related quantitative discipline. Strong prior training in calculus, linear algebra, probability and mathematical statistics is required. Admissions decisions typically consider academic transcripts, letters of recommendation, a statement of purpose outlining study and research interests, and sometimes examples of prior quantitative work or coding projects.

Language requirements depend on the courses chosen; many advanced modules are taught in English, so proficiency in English is normally required. Knowledge of French is an advantage for undertaking internships or projects with local partners but is not always mandatory for admission.

Career prospects

Graduates from the Master in Statistics are well placed for roles that require strong quantitative and computational skills. Typical career paths include data scientist or machine learning engineer in technology and finance, biostatistician or epidemiological analyst in healthcare and pharmaceutical sectors, quantitative analyst or risk modeller in banking and insurance, and statistician or methodologist in public sector agencies and international organisations.

Many graduates also continue to doctoral study (PhD) in statistics, biostatistics, computer science or related fields, or move into research positions at universities and research institutes. The programme’s emphasis on applied projects and collaborations with Geneva-based institutions supports internships and employment opportunities in a city with numerous international organisations and research centres.

Why study at University of Geneva

The University of Geneva offers a research-led statistics education within a multilingual, internationally oriented city. Students benefit from access to active research groups in theoretical and applied statistics, strong computational resources, and opportunities for interdisciplinary collaboration with neighbouring faculties (medicine, economics, computer science) and local research institutions.

Geneva’s concentration of international organisations, hospitals, biotech firms and financial institutions creates a distinctive environment for applied statistical work and internships. The programme’s combination of theoretical depth and practical training is designed to prepare graduates for both demanding professional roles and further research training.