University of Strathclyde

What you'll study

The programme combines core statistical theory with hands-on computation and applied projects. Teaching covers regression and generalized linear models, Bayesian inference, multivariate analysis, time series and forecasting, design of experiments and survival analysis. Emphasis is placed on programming for statistics (R and Python), simulation methods, and the use of statistical software for reproducible analysis.

• Core modules: Regression and Generalized Linear Models; Probability and Inference; Bayesian Methods; Multivariate and Applied Data Analysis; Time Series and Forecasting.
• Computational and applied modules: Statistical Computing and Simulation; Advanced Modelling and Machine Learning; Design and Analysis of Experiments; Survival and Longitudinal Data Analysis.
• Project and dissertation: An independent research project or industry-linked consultancy project where you apply methods to a substantive data problem, develop reproducible workflows and present your findings.
• Optional topics: Depending on availability, options may include statistical methods in clinical trials, actuarial modelling, spatial statistics, or advanced Bayesian computation.

Teaching is delivered through lectures, practical computer labs, seminars and project supervision. Assessment typically combines coursework, programming assignments, exams and the dissertation.

Entry requirements

Applicants are normally expected to hold a good honours degree in mathematics, statistics, economics, engineering, computer science or a closely related discipline. Graduates with a lower classification but substantial quantitative skills or relevant professional experience may also be considered. Prior study in probability and statistics and competence in algebra and calculus are expected.

International applicants must demonstrate English language proficiency in line with the university's standard requirements. Applicants with appropriate professional experience in a quantitative role are encouraged to contact the admissions team to discuss suitability and any preparatory work advised.

Career prospects

Graduates from this programme are prepared for careers that require advanced statistical and data-analytic skills. Common roles include statistician, data scientist, quantitative analyst, biostatistician (particularly in clinical trials and pharma), risk analyst, and research analyst in academia or industry. The programme's computational focus is relevant to roles in finance, healthcare, public sector statistics, technology and consulting.

Many students move into employment with private companies, government agencies and research institutions, or continue to doctoral study in statistics, data science or closely related fields. The dissertation and applied projects offer opportunities to build a portfolio of practical work to show employers.

Why study at University of Strathclyde

Strathclyde has a long-established tradition in quantitative and technical education, with close links to industry across Glasgow and the wider UK. The department emphasises applied, employable skills: you will learn current statistical software and programming practices and have opportunities for industry-facing projects and collaborations.

Studying at Strathclyde gives access to experienced academic staff working in areas from Bayesian methods to industrial statistics and access to computing facilities for large-scale analysis. The university's strong relationships with employers and its careers support help students transition into professional roles or further research.