University of York

What you'll study

This four‑year integrated master's curriculum builds progressively from foundational numerical and programming skills to advanced data science methods and a substantial industrial placement. Early years concentrate on mathematics, programming and statistics, while later years emphasise machine learning, data engineering, probabilistic modelling and scalable computing. Teaching typically combines lectures, small‑group tutorials, laboratory practicals and project work.

• Core topics: programming for data science (Python, software engineering practices), linear algebra, calculus, probability and statistical inference.
• Data and modelling: supervised and unsupervised learning, neural networks, Bayesian methods, time series and probabilistic graphical models.
• Systems and engineering: databases, data pipelines, cloud and distributed computing, big data frameworks and performance optimisation.
• Applied and professional skills: data visualisation, ethics and governance of data, reproducible research, project management and communication.
• Research and capstone: an extended individual research project at master's level, and coursework designed to develop modelling, evaluation and critical reflection skills.
• Industry placement: a year‑long placement in industry or an equivalent professional experience embedded into the programme to provide workplace skills and real‑world problem solving.

Students can expect a mix of assessed coursework, practical assignments, group projects, examinations and an extended dissertation or research project in the final year. Optional modules and specialist topics allow you to tailor your studies towards areas such as natural language processing, computer vision, optimisation or statistical computing.

Entry requirements

The MSci route is designed for applicants with strong preparation in mathematics and computing. Typical successful applicants hold high grades at A‑level or equivalent and demonstrate aptitude for quantitative subjects.

• A‑levels: High grades in relevant subjects with mathematics or further mathematics required. Conditioning offers are based on demonstrated academic ability in quantitative topics.
• International qualifications: Competitive offers for international qualifications (for example, the IB) normally require strong overall scores with higher‑level mathematics.
• Alternative qualifications: Applicants with appropriate foundation or access qualifications, or with substantial work experience in computing/data roles, are considered on an individual basis.
• English language: If your first language is not English, you will need an appropriate English language qualification.

Admissions also take account of personal statements, references and any relevant project or work experience. If you are a mature applicant or have non‑standard qualifications, contact admissions for guidance on how your background maps to the entry criteria.

Career prospects

Graduates of this programme are well placed for roles that require both statistical rigour and software engineering skills. Recent alumni and typical career destinations include data scientist, machine learning engineer, data engineer, quantitative analyst, business intelligence developer and research positions in industry or academia.

The industrial placement year helps build a professional network and practical experience valued by employers in sectors such as finance, healthcare, technology, public policy and consulting. Graduates also use the MSci as preparation for doctoral study in data science, statistics or artificial intelligence.

Why study at University of York

The University of York combines strengths in mathematics, computer science and statistics to deliver an interdisciplinary data science education. The campus environment and research culture support close interaction with active research groups working on machine learning, probabilistic modelling and data‑intensive computing.

• Collaborative teaching that draws on expertise from relevant departments to provide both theoretical depth and applied skills.
• Access to computing facilities, specialised labs and support for developing reproducible and scalable data workflows.
• Established links with industry and a formal placement pathway to gain substantial workplace experience as part of the degree.
• Careers and enterprise services that support CV development, interview preparation and employer engagement specific to data and technology roles.

Taken together, the programme offers a balance of mathematical foundations, practical software skills and professional experience to prepare graduates for technical and analytical careers involving real‑world data challenges.