University of Lancaster

What you'll study

This four-year integrated masters programme builds core skills in statistical modelling, programming, machine learning, data engineering and data visualisation, progressing from foundational topics to advanced, research-led material. Early years concentrate on mathematics for data science, probability, programming (typically Python and relevant libraries), databases and introductory machine learning. Later years add modules in advanced machine learning, deep learning, time series and spatial analysis, scalable data systems, optimisation, data ethics and interpretability, and project-based modules. The industrial experience year is an extended, assessed placement with an employer where students apply techniques to real datasets, develop professional skills and often work on a substantial data-driven project. The final year typically includes an individual research or capstone project that integrates technical methods, empirical evaluation and communication of results for an expert audience.

• Foundations: calculus, linear algebra, probability and statistics, introductory programming
• Core data science: statistical inference, supervised and unsupervised learning, model assessment
• Computational topics: databases, data engineering, big data tools, software engineering for data science
• Advanced methods: deep learning, Bayesian methods, time series, natural language processing or spatial analytics
• Professional development: ethical practice, project management, communication and teamwork
• Industrial experience: assessed year-long placement with an employer or research partner
• Capstone: individual research or industrially-linked project in the final year

Entry requirements

Applicants should have a strong background in mathematics and a good grounding in computing or programming. For UK applicants this typically means high grades at A-level (or equivalent) including Mathematics; for international applicants equivalent qualifications are required. Applicants with a related undergraduate degree may also be considered for the integrated masters in some cases, subject to departmental approval. Admissions selectors look for evidence of quantitative ability, problem-solving, and some exposure to programming or statistical software. Where candidates lack formal programming experience, preparatory material or bridging modules are usually recommended.

Career prospects

Graduates from this programme enter a wide range of roles across industry, government and research. Typical job titles include data scientist, machine learning engineer, data analyst, data engineer, business intelligence analyst and quantitative analyst. The industrial placement year provides practical experience and professional contacts, which often helps graduates secure graduate roles or accelerated progression within employers. The technical and research skills developed on the MSci also provide a strong foundation for doctoral study or specialist technical roles in areas such as computer vision, natural language processing and statistical research.

Why study at University of Lancaster

Lancaster offers a research-active environment with cross-disciplinary activity between mathematics, computer science and the university’s Data Science Institute, providing exposure to both theoretical research and applied projects. The programme’s industrial experience element is supported by established links with regional, national and international employers, and by the university’s careers and placement services. Students benefit from modern computing and laboratory facilities, small-group teaching in later years, and opportunities for collaborative projects with academic researchers. The collegiate campus and dedicated student support services contribute to a structured but flexible learning experience for students pursuing careers in data-driven fields.