Mathematics with Data Science

Mathematics with data science is a degree that develops the theoretical foundations of mathematical thinking alongside the specific skills needed to collect, process, analyse and draw conclusions from large and complex data sets. Data science is not a separate discipline from mathematics but an application of it, and the mathematical depth that a combined degree provides gives you a more secure and adaptable foundation than a purely practical data science programme would. At the University of Sussex, this three-year full-time programme includes a foundation year option and incorporates sandwich, year abroad and work placement opportunities, making it practically enriched and internationally broadened alongside its mathematical and computational core.

You will study pure and applied mathematics, including analysis, algebra, calculus, probability theory and statistical inference, alongside the computational and data science content of programming, machine learning, data visualisation, database management and the mathematical foundations of learning algorithms. The combination develops the ability to work with mathematical rigour while also engaging with the messy, high-dimensional data that real-world problems generate. Sussex's programme is designed for students who want to understand data science at a level where the mathematics makes sense rather than treating its tools as black boxes.

A typical entry tariff of around 104 points is expected, with the foundation year providing an additional entry route.

Graduates are in high demand across finance, technology, healthcare, government, research organisations and any data-intensive industry. Roles include data scientist, machine learning engineer, statistician, quantitative analyst, actuarial analyst and research scientist. The mathematical depth of the degree is particularly valued in roles where understanding the behaviour and limitations of analytical models matters.

Many graduates proceed to postgraduate study in mathematics, statistics, machine learning or data science, and the combination is excellent preparation for doctoral research in any quantitative field.