Loughborough University

What you'll study

The programme combines core modules in mathematics, statistics and machine learning with practical training in data engineering, visualisation and large-scale analytics. Teaching mixes lectures, practical laboratories and project work to build both conceptual understanding and hands-on skills.

• Core topics: statistical inference and modelling, supervised and unsupervised machine learning, probabilistic methods, and optimisation techniques.
• Computing and tools: programming for data science (typically Python and relevant libraries), databases and SQL, pipeline development, and cloud or distributed processing concepts.
• Data engineering and big data: data wrangling, scalable storage and processing, and practical aspects of working with large, heterogeneous datasets.
• Visualisation and communication: techniques for exploratory data analysis, interactive visualisation and translating technical results for decision-makers.
• Optional and contextual modules: domain-focused options may include time-series analysis, natural language processing, computer vision, or specialised electives linked to applied areas such as sports analytics or finance.
• Individual research project / dissertation: a substantial project in which you apply methods learned to real data, often with the opportunity to work with an industrial partner or research group.

Assessment is typically by a combination of coursework, practical assignments, examinations and the final project.

Entry requirements

Applicants are normally expected to hold a good undergraduate degree (usually a UK 2:1 or equivalent) in a quantitative or computing discipline such as mathematics, statistics, computer science, engineering, physics or a related subject. Applicants with a 2:2 plus relevant professional experience or demonstrable programming and numerical skills may be considered on a case-by-case basis.

Applicants should be comfortable with undergraduate-level mathematics (calculus, linear algebra) and basic probability/statistics, and have practical experience of programming or data manipulation. Where necessary, preparatory modules or short online courses are recommended prior to entry.

International applicants must meet the University’s English language requirements; commonly accepted qualifications include IELTS or equivalent assessments demonstrating the ability to study at postgraduate level.

Career prospects

Graduates from this programme go on to a wide range of roles in industry and research. Typical job titles include data scientist, machine-learning engineer, data analyst, data engineer, business intelligence specialist and quantitative analyst.

Employment sectors include technology and software companies, finance and insurance, healthcare and pharmaceuticals, public sector and government, consulting firms, and specialist areas such as sports analytics and engineering. The skills developed also provide a route into further research (PhD) in data science, machine learning, or applied statistics.

Loughborough graduates are often recruited into roles that combine technical modelling with applied problem solving, working in teams to deploy models into production, support data-driven decision-making, or develop new analytics products and services.

Why study at Loughborough University

Loughborough offers a campus-centred experience with strong teaching and considerable emphasis on employability. The University has active research groups and cross-department collaborations in data science, machine learning and analytics, providing access to staff expertise and research-led teaching.

Students benefit from strong industry links and opportunities for collaborative projects or placements, hands-on computing facilities and support services including a dedicated careers team. The university’s connections with nearby industries and a vibrant campus environment create opportunities for networking, internships and applied projects that enhance graduate readiness for the workplace.