University of Turin

What you'll study

The programme blends core theoretical foundations in stochastics and probability with practical data-science tools. You will study probability theory, stochastic processes, statistical inference and applied stochastic modelling alongside modules on machine learning, statistical learning, optimisation and high-dimensional data analysis. Computational components cover programming for data science (typically Python and R), numerical methods for stochastic differential equations, and scalable data-processing techniques.

• Core mathematical subjects: measure-theoretic probability, limit theorems, martingales, stochastic calculus and Markov processes.
• Statistical and machine-learning topics: parametric and non‑parametric inference, Bayesian methods, supervised and unsupervised learning, model selection and cross-validation.
• Computational methods: numerical simulation of stochastic systems, optimisation algorithms, parallel and distributed computing for large datasets.
• Applied modules and laboratories: time series analysis, stochastic modelling in finance and physics, data‑intensive project work using real datasets, and a capstone project or master’s thesis carried out individually or in collaboration with research groups or industry partners.

The curriculum typically includes coursework in the first year, advanced elective modules and project work in the second year, and a final research thesis. Elective choices allow specialisation towards areas such as financial mathematics, computational statistics, network data analysis or research preparation for a PhD.

Entry requirements

Applicants are expected to hold a recognised undergraduate degree in mathematics, statistics, physics, engineering, computer science, quantitative economics or a closely related discipline with substantial mathematics content. Admissions emphasise a strong background in calculus, linear algebra, probability and basic statistics.

• Academic transcript: evidence of relevant undergraduate courses and grades.
• Supporting documents: curriculum vitae, motivation letter explaining your interest and background, and letters of recommendation where available.
• Language proficiency: demonstrated proficiency in the language of instruction. The programme is offered with courses in English; applicants whose prior education was not in English will normally need to provide an approved English test or equivalent certification.
• Additional assessment: applicants may be asked to provide samples of prior quantitative work, take a mathematics assessment or attend an interview, depending on the selection committee’s requirements.

Applicants without a fully matching degree but with significant quantitative experience may be considered and could be required to take bridging courses before or during the programme.

Career prospects

Graduates are equipped for roles that require advanced probabilistic modelling and data-analytic skills. Typical career paths include:

• Data scientist or machine-learning engineer in technology and industry.
• Quantitative analyst, risk modeller or algorithmic trader in finance and insurance.
• Statistician or research scientist in public sector bodies, healthcare analytics and environmental modelling.
• Applied mathematician or modeller in engineering, energy and manufacturing firms.
• Academic or research positions leading to a PhD in probability, statistics or data science.

The programme’s combination of theoretical rigour and applied projects also prepares graduates for consultancy roles and for positions in start-ups or in R&D units of large companies that require advanced stochastic modelling and big‑data capabilities.

Why study at University of Turin

The University of Turin has a long tradition in mathematical sciences and hosts active research groups in probability, statistics and applied mathematics. Studying here gives you access to experienced faculty working on both theoretical stochastics and contemporary data-science problems, as well as to computational facilities and laboratories for hands‑on work.

• Research environment: close links between the mathematics and computer-science departments, with opportunities to participate in research seminars and collaborative projects.
• Industry connections: partnerships with regional and national companies—particularly in finance, automotive and ICT—provide opportunities for internships, collaborative theses and applied placements.
• International outlook: an established international student community and opportunities for exchange programmes and research collaborations across Europe.
• Location and resources: Turin’s scientific and industrial ecosystem offers a good environment for applied data-science careers, with access to public data initiatives, tech hubs and a rich cultural setting.

Overall, the programme is designed to produce graduates who are both mathematically rigorous and practically competent in modern data-science workflows.