University of Strathclyde

What you'll study

This master's blends core statistical theory with specialised modules in financial modelling and econometrics. You study probability and statistical inference, time series and forecasting, and advanced applied statistics alongside finance-focused subjects such as financial econometrics, portfolio analysis, and risk modelling. Practical computing and data analysis are central: you gain hands-on experience with statistical software (commonly R and Python), database handling, Monte Carlo simulation and applied machine learning techniques relevant to financial data.

• Core statistical topics: probability models, statistical inference, regression and multivariate analysis, and stochastic processes.
• Financial/econometric topics: time series and forecasting, financial econometrics, volatility modelling, credit and market risk, and portfolio theory.
• Computing and applied modules: statistical programming, simulation methods, big data techniques for finance and machine learning for predictive modelling.
• Project or dissertation: a substantial independent project or dissertation applying statistical methods to a financial problem, often using real datasets and emphasising reproducible analysis and interpretation.

The programme is usually delivered through lectures, seminars and practical laboratory sessions. Assessment is by a combination of coursework, lab-based assignments, examinations and the final project.

Entry requirements

Applicants are normally expected to hold a good honours degree (typically a UK 2:1 or international equivalent) in a numerate discipline such as mathematics, statistics, economics, engineering, physics or a related subject with substantial quantitative content. Candidates with a lower-class honours degree plus relevant professional experience or a strong quantitative background may also be considered.

Competence in programming and basic statistical computing is desirable; applicants without formal computing experience are advised to demonstrate experience with R, Python, MATLAB or equivalent, or to undertake introductory training before starting. Proof of English language ability is required for applicants whose first language is not English (typical requirements include an academic IELTS or equivalent). Specific entry criteria can vary, so applicants should consult the university for individual assessment.

Career prospects

Graduates are prepared for quantitative roles across the financial services sector and beyond. Typical career destinations include quantitative analyst (quant), risk analyst or modeller, credit risk specialist, portfolio analyst, data scientist in finance, financial engineer and positions in regulatory or compliance analytics.

The skill set developed—advanced statistical methods, time series and econometrics, plus practical programming and data handling—also opens opportunities in consulting, fintech, asset management, insurance and further academic research such as doctoral study.

Why study at University of Strathclyde

Studying at Strathclyde provides access to a research-active mathematics and statistics community with strong links to the financial industry and professional firms. The university places emphasis on applied, career-oriented training and offers computing facilities and specialist software commonly used in finance. Students benefit from industry engagement through guest lectures, employer events and networking opportunities, and from being located in a city with a dynamic business and financial services presence.

The collaborative environment between quantitative departments and business-facing units supports interdisciplinary learning and practical experience, helping graduates make the transition from academic study to professional roles in finance and analytics.