University of Lancaster

What you'll study

The programme combines core data science principles with practical computational skills. Typical topics include statistical inference and probability, supervised and unsupervised machine learning, deep learning fundamentals, data engineering and big‑data architectures, databases and data management, data visualisation and communication, and ethical, legal and societal issues in data science. Teaching emphasises hands-on work: programming (commonly Python and relevant libraries), applied model development, evaluation and deployment, and use of cloud and parallel computing where appropriate.

The course is delivered through modular units culminating in a substantial independent project or dissertation that applies data-science methods to a real-world problem. The project can focus on applied research, an industry problem, or a development exercise producing deployable models or data pipelines.

Entry requirements

Applicants are normally expected to hold an undergraduate degree from a recognised institution. A 2:1 honours degree in a quantitative or computing discipline is the typical requirement, although applicants with a 2:2 plus relevant professional experience may be considered. Acceptable backgrounds include computer science, mathematics, statistics, engineering, physical sciences, or economics with substantial quantitative content.

Applicants without a strong quantitative degree should demonstrate competence in programming and basic statistics through prior study, professional experience, or preparatory modules. International applicants must meet the University's English language requirement. Professional experience in data-related roles is advantageous and can strengthen an application.

Career prospects

Graduates leave prepared for a range of data-focused roles in the private and public sectors. Common job titles include data scientist, machine learning engineer, data analyst, data engineer, business intelligence developer and analytics consultant. The programme also provides a solid foundation for roles that require advanced analytics skills in finance, healthcare, retail, manufacturing, government and technology companies.

Skills gained on the course—statistical modelling, machine learning, data engineering, visualisation and communicating technical results to non-technical audiences—are highly transferable and support progression into senior technical, managerial or specialist research roles, or further academic study such as a PhD.

Why study at University of Lancaster

Lancaster University has a strong reputation for research-led teaching in computing and statistics and offers an online delivery model designed for flexibility and accessibility. The online MSc provides structured learning with tutor support, virtual lab environments for practical work, and opportunities to work on applied projects that mirror industry practice.

Students benefit from the University's links with industry and interdisciplinary research groups, which help inform curriculum content and project opportunities. The programme is supported by academic staff with active research interests in data science and machine learning, and the University provides dedicated online student support services, including academic guidance and careers advice tailored to distance learners.