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Master of Mathematics - MMath
Main Site
Full Time
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. With the Financial Mathematics MMath you’ll study for a further year and bring your BSc degree to master’s standard. This means you’ll be able to get that competitive edge when you apply for jobs without having to go through the application process again after Level 3. Two-thirds of your course is shared with the MMath in Mathematics. This covers several application areas – finance, statistics, operational research (i.e. how maths can be applied to commercial and industrial problems) and numerical analysis. The remaining third covers the key principles of finance. Follow the five-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. 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 and proofs; first order differential equations.
Further skills in differential and integral calculus and applications, including ordinary differential equations.
Mathematical modelling skills: extracting principal features of a problem, formulating and solving models and interpreting results.
Further mathematical modelling skills applied to problems in their original form.
Manipulating mathematical expressions, summation notation, fundamental results, rigour in definitions and proofs, and the language of sets and functions.
Basic linear algebra and complex numbers; eigenvalues and eigenvectors, diagonalization and applications.
Key notions of mathematical probability and techniques for probabilities and expectations; elementary statistics and extracting meaning from data.
Single and multivariable probability; statistical tools for inference, sampling, confidence intervals and hypothesis tests.
Multivariable calculus: Taylor series, extrema, Lagrange multipliers and integration of functions of several variables.
Mathematics of deterministic cash flow streams; optimum allocation of financial resources under uncertainty, financial planning, risk measures and simulation.
Linear algebra tools and applications including linear models; basic algebraic structures and group theory.
Foundational principles of machine learning; regression versus classification, supervised versus unsupervised learning, ethical issues around data and AI.
Continuous and multivariate probability; statistical concepts, confidence intervals and hypothesis tests applied with software.
Skills for planning and obtaining employment, internship or placement; applying methods to an applied problem related to the degree.
Advanced linear and integer programming with industrial software; optimum allocation of financial resources under uncertainty and financial planning models.
Black-Scholes-Merton option pricing, arbitrage pricing, replication and dynamic hedging; lognormal random walk using discrete time models.
Independent learning and critical thinking on a chosen topic; plan, execute and present a substantial piece of work in financial mathematics.
Extract value and insight from large heterogeneous data sets; data mining and machine learning such as clustering, regression, SVMs, boosting, decision trees and neural networks.
Two-term project developing a topic in advanced mathematics, favouring theoretical depth and independent work.
Fixed-income securities markets and key financial products; valuing derivatives in basic models for interest rates and discount bonds.
Risk-neutral valuation and physical versus risk-neutral measures; pricing vanilla and basic barrier options in the geometric Brownian motion model.
Measure-theoretic and probabilistic concepts for modern mathematical finance; stochastic calculus based on Brownian motion.
Research methods, critical thinking and transferable skills; understanding the needs of industry and preparing for the dissertation.
Accommodation (on campus)- 690-1,094 per month
4.2Founded in 1966, but with origins from 1798, Brunel University of London has achieved much in its history. Always an inn...
