University of Lancaster

What you'll study

This programme builds core skills in programming, probability & statistics, data management, and machine learning, progressing from foundational topics to advanced, application‑focused work. The course is structured as three academic years of study plus an additional placement year in industry or the public sector, giving students direct experience of data roles in a workplace setting.

• Year 1 (foundations) — programming fundamentals (typically Python), introductory statistics and probability, mathematics for data science (linear algebra, calculus), basic database concepts, and an introduction to data visualisation and communication.
• Year 2 (core methods) — intermediate machine learning and statistical modelling, data structures and algorithms, relational and non‑relational databases, data engineering basics, experimentation and inferential methods, and coursework emphasising team projects and reproducible analysis.
• Placement year — a full‑year paid or unpaid placement with an employer in industry, government or third sector, where you apply technical skills to real datasets, often gaining experience with cloud platforms, production pipelines and stakeholder engagement.
• Final year (advanced topics and project) — advanced machine learning and deep learning options, big data processing, model deployment and ethics of AI, specialised electives (for example natural language processing, time series, computer vision or optimisation), and a substantial individual or group honours project linked to research or industry problems.

Typical teaching methods include lectures, practical computing labs, group projects, and a major project. Assessment is by a mix of coursework, practical assignments, presentations, exams and the final project.

Entry requirements

Applicants are expected to have a strong background in mathematics and demonstrable programming aptitude. Typical offers consider:

• Advanced level qualifications with good grades, normally including Mathematics (or equivalent quantitative subject).
• Vocational qualifications such as BTECs are considered when they include substantial quantitative or computing content.
• International qualifications are accepted; applicants whose first language is not English will need to meet the University’s English language requirements.
• A personal statement and references that demonstrate interest in data, programming and analytical problem solving; applicants without formal computing experience can be considered if they show clear numerical ability and a willingness to learn coding.

Selection may also take account of relevant work experience, extracurricular projects (such as open‑source contributions or data competitions) and the strength of performance in mathematics or statistics. Check the University’s official admissions pages for specific qualification equivalences and entry guidance.

Career prospects

Graduates go on to a wide range of roles across technology, finance, healthcare, retail, government and consulting. Common job titles include:

• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Data Engineer
• Business Intelligence Developer / Analyst
• Research or PhD study in data science, statistics or AI

The placement year is a significant advantage for employability, offering industry experience, professional contacts and practical examples to use in applications and interviews. Graduates typically work on data pipelines, predictive models, dashboards and deployed analytics systems, and many progress into specialist technical or leadership roles.

Why study at University of Lancaster

Lancaster University combines strong teaching in computing and statistics with active research groups in machine learning, statistics and applied data science. The university supports students with modern computing facilities, high‑performance resources and a programme of guest lectures and industry engagement that helps bridge academic learning and workplace practice.

• Structured placement support through the careers and placement teams to secure and prepare for year‑long industry experience.
• Opportunities to collaborate with research groups and industry partners on projects and final‑year dissertations.
• A collegiate campus environment with centralised student services and extracurricular tech and data societies that provide networking and project opportunities.

Students leave the programme with a balanced combination of theoretical understanding, practical coding and data engineering skills, and the professional experience needed to pursue data roles or further study.