University of Strathclyde

What you'll study

This master's combines core quantitative theory with practical tools used in modern finance. Typical taught topics include stochastic calculus and financial derivatives, numerical methods for valuation, fixed income and interest rate modelling, portfolio theory and risk management, econometrics and time series analysis, and machine learning applications in finance. Practical modules cover programming and implementation using languages and tools such as Python, MATLAB or R, and exposure to market data platforms and trading simulation environments.

The programme is usually delivered through a mix of lectures, seminars, practical computer laboratories and coursework. It culminates in an individual dissertation or a substantial project which applies quantitative methods to a real-world finance problem; some students also have the option to undertake an industry-linked project or consultancy piece with a corporate partner.

Entry requirements

Applicants are normally expected to hold a good honours degree (or equivalent) in a quantitative subject such as mathematics, statistics, actuarial science, physics, engineering, computer science, economics with strong quantitative content, or a related discipline. Many successful applicants hold a 2:1 or higher, while applicants with a lower classification but with substantial, demonstrable quantitative work experience may also be considered.

All applicants whose first language is not English will need to demonstrate proficiency in English. The university publishes specific English language requirements and a range of accepted qualifications; applicants should consult the official University of Strathclyde guidance for up-to-date details.

Career prospects

Graduates from MSc Quantitative Finance typically move into roles that require strong modelling, programming and data-analytic skills. Common career paths include:

• Quantitative analyst / model developer in banks and investment firms
• Risk analyst or risk modeller in financial services and regulatory bodies
• Algorithmic trader or execution strategist
• Portfolio manager or research analyst in asset management
• Model validation and compliance roles
• Data scientist, machine learning engineer or quantitative developer in fintech and technology firms

The programme’s practical emphasis on coding, numerical methods and use of market data platforms helps graduates demonstrate applied skills sought by employers. The university’s careers service and employer engagement activities support CV development, interview preparation and industry networking.

Why study at University of Strathclyde

Studying Quantitative Finance at the University of Strathclyde offers a balance of rigorous theory and hands-on practice delivered in an urban research university with strong industry links. The programme benefits from close interaction between academic staff with expertise in mathematics, statistics and finance and from connections with financial services and fintech companies in Glasgow and beyond.

Students have access to specialist computing facilities and market-data terminals, opportunities for industry-focused projects, and support from an established careers and employability team. The department emphasises transferable technical skills — such as numerical programming, model implementation and data analysis — that are in demand across financial services, technology firms and regulatory organisations.