University of Geneva

What you'll study

The programme combines statistical foundations, machine learning methods, database and computing skills, and business-focused applications. Core topics typically include:

• Statistical modelling and econometrics – regression, time series and inference for decision-making.
• Machine learning and predictive analytics – supervised and unsupervised learning, model evaluation and regularisation techniques.
• Data engineering and databases – SQL, data warehousing, ETL processes and fundamentals of big‑data architectures.
• Optimisation and prescriptive analytics – linear and nonlinear optimisation, simulation and decision analysis.
• Data visualisation and communication – dashboards, storytelling with data and communicating results to non‑technical stakeholders.
• Computing for analytics – practical programming (Python/R), reproducible research, and use of common analytics libraries and tools.
• Ethics, privacy and regulation – responsible use of data, GDPR considerations and ethical frameworks for analytics.
• Capstone project or master thesis – an applied project with an industry partner or a research thesis that integrates technical methods and business impact.

Teaching formats typically mix lectures, hands-on labs, case studies and team projects; assessment commonly includes project reports, presentations and a final thesis or industry capstone.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree or recognised equivalent in a relevant field such as economics, business, computer science, mathematics, statistics, engineering or another quantitative discipline. Typical requirements include:

• Strong quantitative background demonstrated by prior coursework in mathematics, statistics or programming.
• Academic transcripts and a university degree certificate.
• A curriculum vitae and a personal statement or motivation letter outlining suitability and objectives for the programme.
• Letters of recommendation (usually one or two) from academic or professional referees.
• Proof of language proficiency where required: many courses are taught in English, and applicants whose education was not in English may need to provide an accepted English test score; knowledge of French is an advantage for living and working in Geneva but not always mandatory for the programme.

Relevant professional experience, programming samples or a portfolio of analytic work can strengthen an application. Specific document requirements and any minimum grade or prerequisite modules are set by the programme admissions office and should be checked on the university’s official pages.

Career prospects

Graduates enter a wide range of roles that combine analytical skills and business insight. Typical positions include:

• Data scientist or machine learning engineer in finance, technology or healthcare.
• Business or data analyst in consulting firms, multinational corporations and startups.
• Analytics consultant, helping organisations deploy data-driven strategies and tools.
• Operations or supply‑chain analyst using optimisation and forecasting methods.
• Product manager roles with a strong analytics component.
• Positions in public sector and international organisations (NGOs, UN agencies) focused on policy analytics and programme evaluation—particularly relevant given Geneva’s local ecosystem.

The programme’s practical projects and industry contacts facilitate internship and recruitment opportunities with banks, insurers, tech companies, pharmaceutical firms and consulting boutiques. Graduates may also continue to doctoral study in related fields.

Why study at University of Geneva

The University of Geneva offers an academically rigorous programme located in a globally connected city. Key advantages include:

• A strong interdisciplinary environment with links across economics, statistics, computer science and management.
• Proximity to international organisations, multinationals and a dense network of employers in finance, pharmaceuticals and humanitarian sectors—useful for applied projects and internships.
• Access to research-active faculty and laboratory facilities that support applied analytics and collaborative projects.
• An international, multilingual student community and the opportunity to study in a city where French and English are widely used in business and research contexts.
• Practical orientation with industry collaborations, guest lectures and project work designed to prepare students for immediate professional impact.

Prospective students should consult the University of Geneva’s official programme pages for the latest information on course structure, language of instruction and application procedures.