Curious about studying Financial Mathematics with Placement - BSc (Hons)at Brunel University of London? We've gathered all the key details—entry requirements, modules, fees, and more. Hear from real students by checking out reviews, or take the next step by booking an open day to explore it for yourself.
Bachelor of Science (with Honours) - BSc (Hons)
Main Site
Sandwich
Sep 2026
4 Year
Mathematics is a fundamental subject that is critical to our understanding of the world. Through the study of mathematics you’ll advance your problem solving skills, develop your reasoning and increase your analytical thinking. This is why mathematics forms a basis to so many careers. Yes – it is a discipline in its own right, but it is also the thinking behind countless commercial, industrial and technological activities. Mathematical models underpin engineering, the applied sciences, computing and many aspects of management today. Follow the four-year ‘Professional Placement’ degree programme and you‘ll benefit from our extensive experience in helping students to find well-paid work placements with blue-chip companies. Our sandwich students find that their mathematical and transferable skills are in demand in many sectors, both in the UK and abroad. Areas recently offering placements include: accountancy, aviation, banking, defence, finance, insurance, IT (software development, network management and design), management (public and private sector), marketing and telecommunications. The Financial Mathematics BSc covers the application of mathematical methods to financial problems so if you’re looking to work in the fast-moving world of finance or commerce then this is the course for you. Two thirds of the course content focuses on developing your mathematical knowledge and skills. The rest of the course covers financial markets, corporate investment and finance. This programme will meet the educational requirements of the Chartered Mathematician designation, awarded by the Institute of Mathematics and its Applications, when it is followed by subsequent training and experience in employment to obtain equivalent competences to those specified by the Quality Assurance Agency (QAA) for taught master's degrees
Basic results, techniques and elementary functions of differential and integral calculus; rigorous definitions, arguments and proofs and solution techniques of first order differential equations.
Further develops skills in differential and integral calculus and associated applications, including solution techniques of ordinary differential equations.
Developing a facility for mathematical modelling by examining a problem in its original form, extracting principal features, formulating and solving models and interpreting the results.
Second of a pair of modules furthering a facility for mathematical modelling and interpreting results in terms of the original problems.
Manipulating mathematical expressions and using formulae; summation notation, fundamental results, the need for rigour in definitions and proofs, and the language of formal mathematics (sets and functions).
Basic linear algebra and the algebra of complex numbers; eigenvalues and eigenvectors, similar matrices and diagonalization and applications of linear algebra.
Key notions of mathematical probability and techniques for calculating with probabilities and expectations; elementary statistical concepts and extracting meaning from data.
Single and multivariable probability; statistical tools for extracting and inferring meaning from data, sampling and inference, confidence intervals and hypothesis tests.
Ideas and methods of multivariable calculus: Taylor series, extrema, Lagrange multipliers and integration of functions of several variables.
The mathematics of deterministic cash flow streams; optimum allocation of financial resources under uncertainty, financial planning and financial risk measures, with software simulations.
Enlarges the tools of linear algebra and its applications; basic algebraic structures concentrating on group theory and their relevance in real-life applications.
Foundational principles of machine learning with mathematical underpinning and software; the role of ML in AI, regression vs classification, supervised vs unsupervised learning and a prediction machine.
Continuous and multivariate probability; statistical concepts and applications, confidence intervals and hypothesis tests applied in a variety of situations using software.
Skills for planning and obtaining employment and applying techniques, methods, algorithms and theories to an applied problem cognate to the degree.
Placement Year
Modern mathematical finance: the Black-Scholes-Merton theory of option pricing, arbitrage pricing, replication and dynamic hedging and ideas from continuous time stochastic process theory.
Independent learning and critical thinking; planning, executing and presenting a substantial piece of work and written communication of complex ideas in financial mathematics.
Advanced modelling in linear and integer programming using industrial software; optimum allocation of financial resources under uncertainty, financial planning and financial risk measures.
Extracting value and insight from large heterogeneous data sets; data mining and machine learning techniques such as clustering, regression, support vector machines, boosting, decision trees and neural networks.
4.2Founded in 1966, but with origins from 1798, Brunel University of London has achieved much in its history. Always an inn...
