Computational Finance MSc

Help build a successful career as a quantitative analyst on this one-year MSc programme in Computational Finance. You'll benefit from UCL’s renowned expertise in computational statistics and machine learning to acquire the advanced quantitative, modelling and programming skills essential for ‘quant’ roles in trading, research, regulation and risk management. As the financial sector becomes increasingly sophisticated, there is a growing demand for experts with skills spanning mathematics, finance, statistics, and computer science. Join us on this fast-paced MSc to lay the groundwork for a fulfilling career as a quant in just a year. You join the UCL Computer Science community — a world-renowned centre in computational finance and machine learning — to learn advanced quantitative, modelling and programming skills alongside our experts. The syllabus is constantly reviewed to stay ahead of trends. Modules include financial engineering, numerical methods, data science, and machine learning for finance, with optional modules in algorithmic trading, market microstructure, numerical optimisation, networks and systemic risk, and blockchain technologies. This programme is distinctive in that it covers a mix of mathematics, finance, statistics and computer science. Computational finance sits at the intersection of these subjects. Enhancing your skills in all these areas will put you one step ahead when going for quant jobs. This programme is ideal for you if you have an undergraduate degree in mathematics, statistics, physics, engineering, computer science (with exams in mathematics), economics or finance. The Computational Finance MSc requires previous studies in a quantitative subject which includes exams in mathematics. This should cover calculus and linear algebra, and optionally also differential equations, probability, statistics, econometrics and similar. If you aim to work as a ‘quant’ in finance, you will need a desire to extend your skillset with an applied, computational focus.

A minimum of an upper second-class UK bachelor's degree (or international qualification of an equivalent standard) in a relevant discipline with a strong quantitative component evidenced by good performance in mathematics and statistics exams, i.e. with marks in these subjects not below a UK upper second-class or international equivalent level. There is not an exhaustive list of relevant disciplines, but individuals with a background in mathematics, physics, statistics, computer science, engineering, economics, or finance are encouraged to apply.