University of Warwick

What you'll study

The programme balances core theory in probability and statistics with applied and computational techniques. You study measure-theoretic foundations of probability, stochastic processes, and advanced statistical inference alongside modules in computational methods such as Monte Carlo, Markov chain Monte Carlo, and resampling techniques. Coursework typically includes a mixture of lecture-based modules, problem classes, and computer labs.

• Core probability theory: measure-theoretic probability, laws of large numbers, central limit theorems and limit results for stochastic processes.
• Stochastic processes: Markov chains, martingales, Brownian motion and applications to modelling random phenomena.
• Statistical inference: likelihood-based methods, estimation, hypothesis testing, and asymptotic theory.
• Bayesian statistics: prior specification, posterior computation and practical implementation using MCMC.
• Computational statistics: simulation methods, algorithmic implementation in statistical programming languages (for example R and Python), and numerical techniques.
• Applied topics and electives: multivariate analysis, time series, survival analysis, statistical learning, and specialised modules reflecting staff research interests.
• Research project / dissertation: an independent research project supervised by departmental academic staff, developing skills in problem formulation, analysis and scientific communication.

Assessment is through a combination of written examinations, coursework, practical programming assignments and the final dissertation. The programme emphasises mathematical rigour while fostering the computational and modelling skills required for contemporary statistical practice.

Entry requirements

Applicants are expected to hold a strong undergraduate degree in mathematics, statistics, or a closely related discipline demonstrating substantial mathematical content. Typical preparation includes single-variable and multivariable calculus, linear algebra, probability and introductory statistics; prior exposure to real analysis or measure theory is advantageous. Practical programming experience and familiarity with statistical software are desirable.

International applicants should provide a comparable qualification from an accredited institution and meet the university's English language requirements. Selection is based on academic record, references and any supporting statement or evidence of research potential.

Career prospects

Graduates from this MSc are well placed for a range of roles that require strong quantitative and statistical skills. Common career destinations include:

• data scientist and data analyst roles in technology, finance and consulting;
• quantitative analyst and risk modeller positions in banking and insurance;
• statistical roles in pharmaceuticals and healthcare, including biostatistics;
• government and public-sector analyst posts focused on policy evaluation and official statistics;
• research and development positions in industry, or continued academic study at PhD level in statistics, probability or related fields.

The programme’s combination of theory, computation and an independent project prepares graduates both for technically demanding jobs and for further research training.

Why study at University of Warwick

Warwick’s Department of Statistics and the Mathematics Institute have strong research profiles in both foundational probability and modern statistical methodology. Students benefit from teaching by active researchers, a vibrant seminar programme, and opportunities to collaborate across disciplines such as economics, computer science and the life sciences.

The department provides access to computing facilities, statistical software support and career services that connect students with employers and internships. Warwick’s location and institutional links foster collaborations with industry and other research centres, offering a supportive environment for both academic development and employability.