University of Turin

What you'll study

The programme integrates core topics from economics and finance with advanced data-science methods. Core subject areas typically include econometrics and financial econometrics, micro- and macro-economic theory, asset pricing and corporate finance, plus applied modules in time-series analysis, machine learning for economic data, and causal inference.

• Programming and data infrastructure: practical training in Python and R, database management (SQL), data cleaning, and reproducible research workflows.
• Statistical and computational methods: probability and statistics, multivariate analysis, supervised and unsupervised machine learning, natural language processing for finance, and high-frequency data techniques.
• Finance-focused modules: risk modelling, portfolio theory, derivatives pricing, credit risk and performance measurement, often with applied lab sessions or case studies.
• Applied economics: policy evaluation, industrial organisation, public economics or macro-financial linkages depending on elective choices.
• Capstone and research: an applied project, internship or dissertation that requires students to solve a real-world problem using data-science tools and economic/financial theory.

The programme usually mixes lectures, computer labs, seminars and supervised research. Assessment commonly includes coursework, programming assignments, empirical projects and a final thesis or capstone deliverable.

Entry requirements

Applicants are normally expected to hold a relevant undergraduate degree in economics, finance, mathematics, statistics, computer science or a closely related discipline, with a strong quantitative component. Practical experience or coursework in calculus, linear algebra, probability and statistics is advantageous.

• Academic transcript demonstrating solid quantitative performance.
• A personal statement outlining motivations and relevant experience in economics, finance or data analysis.
• Letters of recommendation from academic or professional referees.
• Evidence of programming or quantitative skills (examples of coursework, projects or a brief coding portfolio may be requested).
• International applicants will be asked to provide proof of English language proficiency where required.

Selection may consider relevant work experience, prior research, or preparatory courses for applicants from non-standard backgrounds. Specific documentation and formal requirements are provided by the University at application.

Career prospects

Graduates are prepared for roles that combine economic understanding with data-driven decision-making. Typical career destinations include:

• Data scientist or quantitative analyst in banks, asset management firms and hedge funds.
• Risk analyst, credit modeller or pricing specialist in financial institutions and insurance companies.
• Economic consultant or analyst in professional services firms and think tanks, using empirical methods to advise businesses and policymakers.
• Business intelligence and analytics roles in industry where financial and economic insights are required.
• Further academic research leading to PhD programmes in economics, finance, statistics or computer science.

Students benefit from developing a portfolio of empirical projects and programming demonstrations that are attractive to employers across finance, consulting, public sector and technology firms.

Why study at University of Turin

The University of Turin offers a longstanding academic tradition with departments active in economics, statistics and computational sciences, creating a multidisciplinary environment suited to data-driven finance. The city of Turin has a diverse economic base and a regional finance and industry network that can support internships, applied projects and local collaborations.

Students can expect access to faculty with expertise in econometrics, finance and machine learning, computing facilities and seminar series that bring together academic researchers and industry practitioners. The University also provides career services, research centres and opportunities to work on applied projects that bridge theory and practice.