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Mathematics MMath

Key Information

Course code

G100

G101 with placement

Start date

September

Placement available

Mode of study

4 years full-time

5 years full-time with placement

Fees

2025/26

UK £9,535

International £22,320

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Entry requirements

ABB - ABC inc Maths or Further Maths at grade A (A-level)

DDM and A-level in Maths or Further Maths at grade A. (BTEC)

30 (IB)

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Overview

Ranked no.4 in London for student satisfaction in mathematics by The Complete University Guide 2024.

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. Mathematical models underpin engineering, the applied sciences, computing and many aspects of management today. With the 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.

You’ll study many aspects of pure and applied mathematics, together with general concepts of mathematical modelling. When it comes to the application of mathematics, we cover finance, statistics, operational research (how maths can be applied to commercial and industrial problems), numerical analysis (the approximate solution of very hard problems) and mechanics.

In your final year you’ll be able to specialise in areas of mathematics that you’re particularly interested in. You’ll produce a substantial research project under the guidance of a tutor in Level 3 and have the opportunity to build upon this as a MMath student.

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.

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Course content

At Brunel we aim to make your transition into the university style of learning as easy as possible. So in the first few weeks of Level 1 you’ll start your learning in small groups of about 20 students. During your first year you’ll learn how to apply your mathematical knowledge to the real world.

By Level 2 you’ll be learning through lectures, seminars and in computer labs, and an individual piece of course-work will account for one third of Level 3.

You’ll be able to select from a large number of projects covering a wide range of mathematical areas and applications. Your project will be supervised by a staff member. You’ll emphasise real applications or abstract theories, using theoretical and/or computational tools. If you’ve completed a placement you will be able to choose a project associated with your work experience. Examples of project titles are:

  • The very famous ‘travelling salesman problem’ (also known as ‘the lazy waiter’!);
  • Simulations of iterated Prisoner’s Dilemma and game theory
  • The mathematics of complex networks such as the web or Facebook
  • Applications of statistics to the Premier League, police complaints data and climate change

In your master’s year (this is your final year of the programme and called Level 5), you’ll have the chance to build upon your Level 3 project and to specialise in areas of mathematics that particularly interest you.

Compulsory

  • Fundamentals of Mathematics

    Aims to manipulate mathematical expressions accurately, as well as recall and use mathematical formulae in areas of interest for Year 1. To develop skills in handling summation notation. To introduce students to fundamental results in mathematics. To develop an understanding of the need for rigour in definitions and proofs. To introduce the language of formal mathematics, in particular sets and functions.

  • Calculus 1

    Aims to familiarise students with the basic results, techniques and elementary functions of differential and integral calculus as well as some simple applications. To introduce students to rigorous definitions, arguments and proofs through many simple examples. To develop students’ manipulative skills in performing operations in differential calculus through work on many simple examples, including solution techniques of first order differential equations

  • Calculus 2

    Aims to further develop skills in differential and integral calculus and associated applications. To further develop students’ manipulative skills in performing operations in differential calculus through work on examples, including solution techniques of ordinary differential equations.

  • Elements of Applied Mathematics 2

    Second of a pair of modules developing modelling skills. Furthering a facility for mathematical modelling by examining a problem in its original form, extracting the principal features, formulating and solving appropriate mathematical models and interpreting the results in terms of the original problems.

  • Elements of Applied Mathematics 1

    First of a pair of modules developing modelling skills. To develop a facility for mathematical modelling by examining a problem in its original form, extracting the principal features, formulating and solving appropriate mathematical models and interpreting the results in terms of the original problems. Problems will be chosen from a wide range of applications.

  • Linear Algebra

    Aims to develop understanding and technical skills in linear algebra with a particular focus on systems of linear equations, vector and matrix algebra, eigenvalues and eigenvectors of matrices, and diagonalization.vStudents will practise on large systems of linear equations using software.

  • Probability and Statistics 1

    Aims to introduce key notions of mathematical probability and develop techniques for calculating with probabilities and expectations: to lay the foundation of subsequent modules in probability and statistics. To obtain a solid grounding in some applications of probability and elementary statistical concepts, with applications. To develop skills in extracting meaning from data, presenting data graphically and summarising results in writing.

Compulsory

  • Linear and Abstract Algebra

    Aims to enlarge the set of technical tools of linear algebra and develop its applications to different problems, including construction and analysis of linear models. To introduce basic algebraic structures, concentrating on group theory. To exemplify their power, relevance and importance in real life applications.

  • Scientific Computing

    Aims to introduce the need for approximation in mathematics and/or statistics in the context of important pure and/or applied problems that cannot be solved exactly ‘by hand’. To introduce Computer Algebra as a tool for symbolic ‘exact’ computation as well as aid for approximation of otherwise intractable problems. To introduce the concept of simulation and its use in modelling.

  • Calculus 3

    Aims to develop ideas and methods of multivariable calculus, including Taylor series, extrema, the use of Lagrange multipliers, and the integration of functions of several variables. To understand the extension from single variable to several variables of basic concepts such as continuity and differentiability.

  • Probability and Statistics 2

    Aims to further develop skills in continuous and multivariate probability. To impart an understanding of statistical concepts and applications. To develop skills in extracting meaning from data, using software, and presenting results. To develop the concepts of confidence intervals and hypothesis tests. To apply these concepts in a variety of situations and to interpret the results of these procedures.

  • Applied Statistics

    Aims to introduce and consolidate the notions of single and multivariable probability. Introduce statistical tools and explain their use in extracting and/or inferring meaning from data. To introduce sampling and inference, along with confidence intervals and hypothesis tests.

  • Professional Development and Project Work

    Aims to develop skills required for planning and obtaining employment, whether it is an internship, a placement or a graduate job, in a field related to the student degree programme. To see the development of these skills as a continuing, managed, lifelong process. To apply techniques, methods, algorithms and/or theories to an applied problem cognate to your degree studies.

Optional

  • Discrete Mathematics

    Graphs serve as a background for many important problems in real world applications. This gives an understanding of this area of discrete mathematics, and develops a knowledge of graph theory applications. Also, to introduce operational research optimisation modelling and problem solving and, in particular, linear programming (LP) problems. To introduce algorithms for numerical optimisation and the modelling of random events.

  • Statistical Programming for Data Analytics

    Aims to develop the capacity to use a statistics programming language to perform computational statistics along with a variety of computational statistics methods to tackle mathematical and statistical tasks. In particular, to study some simple mathematical and statistical models, fit these statistical models to real data, make predictions from the models, and report the results.

Compulsory

  • Complex Variable Methods and Applications
    This module aims to develop the students' ability to manipulate expressions involving complex quantities and to develop their understanding of analytic functions with representations involving contour integrals and series, and to enable students to evaluate certain definite integrals using contour integration.
  • Final Year Project
    This module aims to stimulate independent learning and critical thinking by the student, both as a means for studying their chosen topic and for approaching other real-life problems, to enable the student to plan and execute a major piece of work with limited input from a more experienced worker, and to give the student experience in the written communication of complex ideas and concepts and the presentation of a substantial piece of work.

Optional

  • Encryption and Data Compression
    This module aims to familiarise students with techniques widely used in data encryption and compression, and to study mathematical and algorithmic issues and their implications for encryption and compression in practice.
  • Numerical Methods for Differential Equations
    This module aims to introduce numerical methods used to solve problems in financial mathematics, in particular option pricing, and implement them. Most examples treated will be taken from finance, but the module will be suitable for students mostly interested in advanced numerical methods, in particular finite differences algorithms used to approximate solutions of PDEs. The Matlab implementations of the algorithms will be an important part of the module.
  • Ordinary and Partial Differential Equations
    This module aims to introduce students to the mathematics of differential equations; techniques of analysing such equations, and methods of solving them, exactly or approximately.
  • Stochastic Models
    This module aims to introduce students to the concept of a stochastic process, so that they may develop an understanding of the theory underlying some of the standard models and acquire knowledge of methods of applying these models to solve problems. Students will further develop their general ability to think abstractly, to generalise, to formulate and structure stochastic problems, and to apply their knowledge of analytical and numerical mathematical techniques to solving a variety of problems in stochastic modelling.
  • Practical Machine Learning
  • Experimental Design and Regression
  • Deep Learning

    Within this module, an in-depth introduction will be provided to the area of learning using deep neural networks. A wide variety of the architectures of deep neural networks and their learning methods will be covered, including convolutional networks, recurrent networks, generative models and deep reinforcement learning etc. The main focus of the module is to develop students’ skill in analysing of problem requirements, applying appropriate deep learning methods to real-world problems, and evaluating the effectiveness of the adopted approach.

  • Decision Making in the Face of Risk

Compulsory

  • Probability and Stochastics
    This module aims to equip students with the basic measure-theoretic and probabilistic concepts and techniques needed for them to be able to apply the modern mathematical theory of finance, and to enable students to use methods of stochastic calculus based on Brownian motion in such a way that they are able to carry out the necessary mathematical manipulations and calculations required for use and critical assessment of the various financial models introduced in other modules of the programme.
  • Computer Intensive Statistical Methods
    This module aims to introduce the students to a range of computational intensive statistical methods, to further develop their skills in correct interpretation and clear reporting of results, and to enable the students to create algorithms for regression models (parametric regression and nonparametric regression) to cope with massive data.
  • Quantitative Data Analysis
    The aim of this module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. Content covers a practical understanding of core methods in data science application and research, such as bivariate and multivariate methods, regression and graphical models. A focus is also placed on learning to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.

Optional

  • Fundamentals of Machine Learning
    This module aims to equip students with the knowledge and ability to use modern regression and classification methods with different types of data, to enable students to apply a range of models and tools to variable selection and model selection.
  • Time Series Modelling
    This module aims to equip students with the ability to employ different methods for modelling and forecasting time series data, in particular in the context of financial data and forecasting financial risk, and to enable students to apply a range of models and tools to make financial decisions such as risk assessment.
  • Option Pricing Theory
    This module aims to equip students with the notion of risk-neutral valuation and the relation between physical and risk-neutral probability measures in the Brownian context, and to enable students to price vanilla options and basic barrier options in the geometric Brownian motion model.
  • Interest Rate Theory
    The purpose of this module is to equip students with a basic familiarity of fixed-income securities markets and, in particular, with the structures of key financial products traded in such markets. It aims to enable students to value derivatives in a number of basic models for interest rates and discount bonds.
  • Big Data Analytics
    The aim of this module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques, such as clustering, regression, support vector machines, boosting, decision trees and neural networks.

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This course has a placement option. Find out more about work placements available.


Please note that all modules are subject to change.

Careers and your future

Career prospects for mathematicians are excellent and with the MMath you’ll be one step ahead of your peers when it comes to applying for jobs.  Maybe you want to pursue a career that specifically uses your mathematical or statistical skills or would prefer a more general career, such as management or consultancy. Either way you’ll possess key skills that are highly sought after by business – in fact any industry that uses modelling, simulation, cryptography, forecasting, statistics, risk analysis and probability.

Our combination of work experience and up-to-date teaching means that you will be well-equipped to follow the career you want after graduation.

These are some of the areas where a maths degree is valued highly:

  • Finance: banking, accountancy, actuarial, tax, underwriter, pensions, insurance
  • Medicine: medical statistics, medical and epidemiological research, pharmaceutical research
  • Design: engineering design, computer games
  • Science: biotechnology, meteorology, oceanography, pure and applied research and development
  • Civil Service: scientists (‘Fast Stream’, DSTL, DESG), GCHQ, security service, statisticians
  • Business: logistics, financial analysis, marketing, market research, sales oil industry, management consultancy, operational research
  • IT: Systems analysis, research
  • Engineering: aerospace, building design, transport planning, telecommunications, surveying.

UK entry requirements

2025/26 entry

  • GCE A-level ABB-ABC, including grade A in Mathematics or Further Mathematics.
  • BTEC Level 3 National Extended Diploma (RQF) / BTEC Level 3 Extended Diploma (QCF) DDM in any subject plus A-level Mathematics or Further Mathematics grade A.
  • BTEC Level 3 National Diploma DM in any subject with an A-level Mathematics or Further Mathematics at grade A.
  • BTEC Level 3 National Extended Certificate D in any subject with A-levels grades AC including grade A in  Mathematics or Further Mathematics.
  • International Baccalaureate Diploma 30 points, including 6 in Higher Level Mathematics (Analysis and Approaches) or Mathematics (Applications and Interpretation) 
  • Access to Higher Education Diploma Access courses are not accepted for MMaths entry. Access students are encouraged to apply for the BSc degree in this subject and if they achieve sufficient grades on year one of the course they can discuss transferring to the MMath.
  • T levels :  not accepted.

Five GCSEs at grade C or grade 4 and above are also required, to include Maths and English Language.

Brunel University London is committed to raising the aspirations of our applicants and students. We will fully review your UCAS application and, where we’re able to offer a place, this will be personalised to you based on your application and education journey.

Please check our Admissions pages for more information on other factors we use to assess applicants as well as our full GCSE requirements and accepted equivalencies in place of GCSEs.

EU and International entry requirements

English language requirements

  • IELTS: 6 (min 5.5 in all areas)
  • Pearson: 59 (59 in all sub scores)
  • BrunELT: 58% (min 55% in all areas)
  • TOEFL: 77 (min R18, L17, S20, W17) 

You can find out more about the qualifications we accept on our English Language Requirements page.

Should you wish to take a pre-sessional English course to improve your English prior to starting your degree course, you must sit the test at an approved SELT provider for the same reason. We offer our own BrunELT English test and have pre-sessional English language courses for students who do not meet requirements or who wish to improve their English. You can find out more information on English courses and test options through our Brunel Language Centre.

Please check our Admissions pages for more information on other factors we use to assess applicants. This information is for guidance only and each application is assessed on a case-by-case basis. Entry requirements are subject to review, and may change.

Fees and funding

2025/26 entry

UK

£9,535 full-time

£1,385 placement year

International

£22,320 full-time

£1,385 placement year

Fees quoted are per year and may be subject to an annual increase. Home undergraduate student fees are regulated and are currently capped at £9,535 per year; any changes will be subject to changes in government policy. International fees will increase annually, by no more than 5% or RPI (Retail Price Index), whichever is the greater.

More information on any additional course-related costs.

See our fees and funding page for full details of undergraduate scholarships available to Brunel applicants.

Please refer to the scholarships pages to view discounts available to eligible EU undergraduate applicants.

Teaching and learning

Lectures will primarily be delivered in-person on-campus, though some may be delivered online either as pre-recorded or live sessions. The expectation is that you will attend all timetabled on-campus lectures, and that online lectures will be viewed by you in advance of related on-campus activities.

Tutorials & discussion-based sessions will primarily be delivered in-person on campus, though some may be delivered online in order to supplement on-campus learning. You will attend all timetabled on-campus or online tutorials.

Computing Labs will primarily be delivered in-person on campus, though some may be delivered online in order to supplement on-campus learning. The expectation is that you will attend all timetabled on-campus or online computing labs and be provided with access to the specialised software required.

Support/resources: Learning materials for every module will be made available online, through the University’s Virtual Learning Environment.

Assessments will be varied, and may include: CAA (computer aided assessment) tests, written coursework assessments (including software tasks), presentations (in-person or video presentations) and written examinations. You will be expected to attend assessments in-person on campus. 

Access to a laptop or desktop PC is required for joining online activities, completing coursework and digital exams, and a minimum specification can be found here.

We have computers available across campus for your use and laptop loan schemes to support you through your studies. You can find out more here.

Mathematics at Brunel has an active and dynamic research centre and many of our lecturers are widely published and highly recognised in their fields. Their work is frequently supported by external grants and contracts with leading industry and government establishments. Lecturers are consequently at the frontiers of the subject and in active contact with modern users of mathematics. This means that you can be assured that our academics are teaching you a truly up-to-date degree and you’ll benefit from a wide range of expertise across the different areas of mathematics.

Your academics are always here to help and offer support. There are maths and numeracy workshops run throughout the year where you can seek support in linear algebra, complex calculus, LaTeX, MATLAB and more. You’ll also benefit from the extra support offered to you at our Maths Café. Here you can bring along any maths-related questions and receive one-to-one help in an informal setting.

Should you need any non-academic support during your time at Brunel, the Student Support and Welfare Team are here to help.

Assessment and feedback

The ‘exams to coursework’ ratio is around 50:50 in Level 1, increasing to 70:30 in Level 3.Level 2 will count towards 20% of your degree. Level 3 and your master's year will count for 40% each.