Mathematics for Data Science

Mathematics for data science is a degree that takes seriously the quantitative foundations on which modern data analysis depends. The explosion of data generated by digital systems, sensors, social media and financial markets has created an extraordinary demand for people who can not only use data analysis tools but understand the mathematical principles that make those tools work, evaluate their outputs critically, and design new approaches when existing ones are inadequate. A mathematics degree specialising in data science gives you exactly this combination of rigour and applicability.

At Brunel University London this three-year full-time programme is designed around the understanding, noted in the course description, that big data is a major phenomenon of the present century and that demand is growing for analysts who can collate, interpret and draw real value from complex datasets. You will study the mathematical foundations of statistics, probability, linear algebra, calculus and optimisation that underpin data science methods, alongside computational skills in programming and data analysis tools. You will learn to build and evaluate statistical models, to apply machine learning techniques with an understanding of the mathematics behind them, and to work with large and messy real-world datasets.

The London setting gives you access to one of the world's major financial and technology centres, with a correspondingly rich range of potential employers and networks in data-intensive industries.

Graduates pursue careers as data scientists, data analysts, quantitative analysts, statisticians, machine learning engineers and research scientists across finance, technology, healthcare, retail, government and research institutions. The mathematical depth of this degree distinguishes graduates from those who have learned data science tools without the underlying theory, making them more adaptable to new methods and more capable of working on novel problems. Postgraduate study in statistics, data science, machine learning or applied mathematics is also a common next step.