Mathematics with Data Science with Foundation Year

Mathematics with data science is a degree that combines the rigour and depth of pure and applied mathematics with the modern computational and analytical tools used to extract insight from large datasets. Mathematics provides the theoretical foundations: the proof-based reasoning, the calculus, linear algebra, and statistics that underpin everything from machine learning to financial modelling. Data science adds the applied dimension, teaching you to work with real data using programming, statistical inference, and the algorithms that power contemporary technology.

Together they form a powerful combination for a world in which data is everywhere and mathematical literacy is essential to making sense of it.

At the University of Surrey, this programme encompasses the latest advances in mathematics for data science, giving you a strong foundation in core mathematics including equations and classical dynamics, while also allowing you to explore optional areas such as quantum mechanics and the mathematics of weather and climate. You will develop competence in programming and data analysis tools alongside the theoretical mathematical frameworks that give those tools their meaning. The programme runs over four years and includes a foundation year, which provides a solid preparation in mathematics and related subjects before the main degree begins, making it a strong option for students who wish to build or consolidate their mathematical foundations.

Surrey's strong links to industry and its location in the South East mean you will study in an environment with good connections to technology, engineering, and financial sectors. The programme develops both the depth of mathematical thinking and the practical data skills that employers across a wide range of industries are actively seeking.

Graduates from mathematics with data science programmes are sought after across many sectors. Common career destinations include data science and analytics roles in technology, finance, healthcare, and the public sector; software development; actuarial work; financial modelling; and research positions in industry and academia. Many graduates also pursue postgraduate study in statistics, machine learning, applied mathematics, financial mathematics, or quantitative finance.