University of Leicester

What you'll study

The programme begins with a foundation year that builds essential academic skills and introduces core mathematical and computing concepts to prepare you for the full BSc. In the subsequent three years you will study a mix of pure and applied mathematics together with data-science and AI subjects. Teaching methods include lectures, seminars, computer labs and project work, and assessment is typically a combination of examinations, coursework and group or individual projects.

• Foundation year (examples of topics): algebra and calculus foundations, introductory statistics, programming fundamentals (usually Python), academic study skills and mathematics for computing.
• Year 1–3 core topics: real analysis and linear algebra; multivariable calculus; probability theory and mathematical statistics; numerical methods and scientific computing; discrete mathematics and optimisation.
• Data science & AI topics: introductory and advanced machine learning, statistical learning, data mining, data visualisation, databases and data management, practical AI techniques and neural networks, natural language processing and ethics in AI.
• Practical skills and project: coursework-based data projects, independent final-year dissertation or project applying mathematical and computational techniques to a real dataset or modelling problem; training in Python, R and common data libraries/packages.
• Optional modules: depending on availability you may choose options such as financial mathematics, cryptography, modelling for the sciences, computational methods, or modules from neighbouring disciplines like computer science.

Entry requirements

This programme is aimed at applicants who require a preparatory year before starting the standard undergraduate degree. Typical applicants include those without the conventional A-level or equivalent qualifications in mathematics or computing, mature students returning to study, and many international applicants whose prior qualifications need bridging.

• GCSEs: usually passes in English and mathematics at a standard equivalent to grade C/4 or above are expected.
• Higher qualifications: applicants who do not hold A-levels, BTECs or equivalent in mathematics may be considered for the foundation route. Successful completion of the foundation year with the required progression criteria is needed to continue to the three-year BSc programme.
• International applicants: recognised international school-leaving qualifications are accepted and English language proficiency at the university’s required level will be necessary if English is not your first language.
• Mature and alternative routes: applicants with relevant work experience or non-traditional qualifications are encouraged to apply and may be considered on an individual basis.

Career prospects

Graduates combine strong mathematical reasoning with practical data science and AI skills, making them attractive to a wide range of employers. Typical career destinations include roles as data analysts, data scientists, machine-learning engineers, quantitative analysts in finance, software developers, and roles in business intelligence and consultancy. Graduates also continue to postgraduate study in specialised mathematics, statistics, data science or AI programmes, or pursue professional accreditation in areas such as actuarial science.

The programme’s emphasis on practical programming, project work and communication helps develop transferable skills valued in industry, including problem solving, critical thinking and the ability to work with large datasets.

Why study at University of Leicester

The University of Leicester offers strong mathematical teaching combined with applied data-science training. The School of Mathematics has a broad teaching and research profile across pure and applied areas, and students benefit from experienced academic staff who supervise final-year projects and dissertations.

Facilities include modern computing labs and access to common data-science software and libraries. The university provides careers and employability support, opportunities for placements or work-related learning elements, and routes into postgraduate study through internal and external research connections. Being based in a compact campus and city environment also gives students access to local employers, cultural amenities and a supportive student community.