A foundation year can develop your skills and make it easier to get started at university. It is an extra year of study at the start of your course that leads in to a full degree programme. It’s a great option if:You don’t have the grades for a full degree courseYou have non-traditional qualifications or experienceYou’re starting university after some time away from educationYou’re looking for more support during the transition into university study.

During a foundation year you’ll learn about your chosen subject, develop your study skills and get used to university life. On successful completion of your foundation year, you’ll be ready to progress to the first year of your degree course.As a foundation year student, you’ll be a full student of the University and part of our community. You’ll have access to all our campus facilities and support.

Encompassing the latest advances in both disciplines, our courses enable you to develop skills such as creative problem-solving and logical reasoning, which are in great demand in a range of sectors.As a mathematics undergraduate at Surrey, you’ll be part of a vibrant and friendly community. You’ll benefit from a personal tutor, small group teaching, and a lively, research-active learning environment. You can also take part in our award-winning Professional Training placements scheme, which prepare students for roles in various sectors.

Our course helps you build on your current knowledge and gain a strong foundation across the fundamentals of mathematics, data science and statistics. This includes algebra, analysis, probability and advanced calculus. Data science and statistics modules together comprise between one-quarter and one-third of the programme.In your first year, you’ll also develop your mathematical programming skills using Python, which will be further developed in Year 2, and gain experience with R programming.In Year 3, you’ll be able to choose from a broad range of optional modules in topics such as experimental design, asset pricing theory, mathematical theory of data science, Bayesian statistics, and graphs and networks.