Undergraduate Open Days
Join us at the Medway campus on Saturday 24 June or the Canterbury campus on Saturday 1 July. Hear from staff and students about our courses, find out about our accommodation and see our stunning campuses for yourself.
What if you could learn the analytical skills needed by employers, while exploring the cutting-edge of mathematical science? A Mathematics degree from The University of Kent gives you the best of both worlds.
We know you love maths. So our courses are built on the research expertise of our world-leading mathematicians. And we know you care about your career. So we rapidly adapt what we teach to reflect the fast-moving graduate employment market.
Why study a Mathematics degree at Kent
- You’ll be taught by outstanding academics working at the scientific boundaries of pure & applied mathematics and statistics
- You’ll learn skills that are highly-valued by the best employers in business, finance, computing and engineering
- You'll use industry-standard software like Maple, MATLAB, R and Python.
- You'll see how mathematics is crucial in data science, conservation, and healthcare.
- You’ll study in award-winning classrooms and breakout spaces that have been specially designed for mathematics
- You'll be able to join brilliant Student Societies for specialist workshops, revision sessions, socials and networking events.
What you’ll study
In the first year you’ll study a mixture of pure & applied maths and statistics, setting you up to create the degree that you want. Small group tutorials help to bridge the gap between school and university and develop your problem-solving skills.
In the second year you build on this base, moving into advanced topics like analysis, number theory, numerical methods and statistical modelling.
In your final year you get to choose. You can specialise in highly academic topics which typically include: topology, complex analysis, non-linear systems and quantum mechanics. You can look at application areas such as machine learning, games & strategy and finance. Or if you prefer, you can do a bit of both.
As you progress, you can tailor your degree to your interests through our optional modules. You can also take a project module and, under supervision, research a current topic.
Year in industry
You can choose to take this course with a Year in Industry. If you’d like to apply for an industrial year in a real job, we’ll work with the company to make sure you’re learning what you need to know and we have a dedicated Placements Team who’ll support you every step of the way.
If you don’t have the qualifications to start your degree right away you may be able to take a four-year course including a Foundation Year. On this “Year 0” our academic staff will teach you the mathematics you need, right here, on campus, in our thriving student community.
This degree meets the educational requirements of the Chartered Mathematician designation, awarded by the Institute of Mathematics and its Applications (IMA), when it is followed by subsequent training and experience in employment to obtain equivalent competencies to those specified by the Quality Assurance Agency (QAA) for taught master’s degrees.
The School is very welcoming and there is lots of support available if you need it. It's a good environment to learn in.
Clarissa Baramki - Mathematics BSc
The University will consider applications from students offering a wide range of qualifications. All applications are assessed on an individual basis but some of our typical requirements are listed below. Students offering qualifications not listed are welcome to contact our Admissions Team for further advice. Please also see our general entry requirements.
AAB including Mathematics grade A (not Use of Mathematics). Only one of General Studies or Critical Thinking can count as a third A level.
Access to HE Diploma
The University will not necessarily make conditional offers to all Access candidates but will continue to assess them on an individual basis.
If we make you an offer, you will need to obtain/pass the overall Access to Higher Education Diploma and may also be required to obtain a proportion of the total level 3 credits and/or credits in particular subjects at merit grade or above.
The University will consider applicants holding BTEC National Diploma and Extended National Diploma Qualifications (QCF; NQF; OCR) on a case-by-case basis. Please contact us for further advice on your individual circumstances.
34 points overall or 17 points at HL including Mathematics 6 at HL .
International Foundation Programme
The University will consider applicants holding T level qualifications in subjects closely aligned to the course.
The University receives applications from over 140 different nationalities and consequently will consider applications from prospective students offering a wide range of international qualifications. Our International Development Office will be happy to advise prospective students on entry requirements. See our International Student website for further information about our country-specific requirements.
Please note that if you need to increase your level of qualification ready for undergraduate study, the School of Mathematics, Statistics and Actuarial Science offers a foundation year.
English Language Requirements
Please see our English language entry requirements web page.
Please note that if you do not meet our English language requirements, we offer a number of 'pre-sessional' courses in English for Academic Purposes. You attend these courses before starting your degree programme.
Duration: 3 years full-time
The following modules are indicative of those offered on this programme. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.
On most programmes, you study a combination of compulsory and optional modules. You may also be able to take ‘elective’ modules from other programmes so you can customise your programme and explore other subjects that interest you.
This module introduces widely-used mathematical methods for matrix operations, calculus of functions of a single variable, and scalar ordinary differential equations (ODEs). The emphasis is on the practical use of these methods; key theorems are stated but not proved at this stage. Tutorials and Maple worksheets will be used to support taught material.
Introduction to Probability. Concepts of events and sample space. Set theoretic description of probability, axioms of probability, interpretations of probability (objective and subjective probability).
Theory for unstructured sample spaces. Addition law for mutually exclusive events. Conditional probability. Independence. Law of total probability. Bayes' theorem. Permutations and combinations. Inclusion-Exclusion formula.
Discrete random variables. Concept of random variable (r.v.) and their distribution. Discrete r.v.: Probability function (p.f.). (Cumulative) distribution function (c.d.f.). Mean and variance of a discrete r.v. Examples: Binomial, Poisson, Geometric.
Continuous random variables. Probability density function; mean and variance; exponential, uniform and normal distributions; normal approximations: standardisation of the normal and use of tables. Transformation of a single r.v.
Joint distributions. Discrete r.v.'s; independent random variables; expectation and its application.
Generating functions. Idea of generating functions. Probability generating functions (pgfs) and moment generating functions (mgfs). Finding moments from pgfs and mgfs. Sums of independent random variables.
Laws of Large Numbers. Weak law of large numbers. Central Limit Theorem.
Introduction to R and investigating data sets. Basic use of R (Input and manipulation of data). Graphical representations of data. Numerical summaries of data.
Sampling and sampling distributions. ?² distribution. t-distribution. F-distribution. Definition of sampling distribution. Standard error. Sampling distribution of sample mean (for arbitrary distributions) and sample variance (for normal distribution) .
Point estimation. Principles. Unbiased estimators. Bias, Likelihood estimation for samples of discrete r.v.s
Interval estimation. Concept. One-sided/two-sided confidence intervals. Examples for population mean, population variance (with normal data) and proportion.
Hypothesis testing. Concept. Type I and II errors, size, p-values and power function. One-sample test, two sample test and paired sample test. Examples for population mean and population variance for normal data. Testing hypotheses for a proportion with large n. Link between hypothesis test and confidence interval. Goodness-of-fit testing.
Association between variables. Product moment and rank correlation coefficients. Two-way contingency tables. ?² test of independence.
Real Numbers: Rational and real numbers, absolute value and metric structure on the real numbers, induction, infimum and supremum.
Limits of Sequences: Sequences, definition of convergence, epsilon terminology, uniqueness, algebra of limits, comparison principles, standard limits, subsequences and non-existence of limits, convergence to infinity.
Completeness Properties: Cantor's Intersection Theorem, limit points, Bolzano-Weierstrass theorem, Cauchy sequences.
Continuity of Functions: Functions and basic definitions, limits of functions, continuity and epsilon terminology, sequential continuity, Intermediate Value Theorem.
Differentiation: Definition of the derivative, product rule, quotient rule and chain rule, derivatives and local properties, Mean Value Theorem, L'Hospital's Rule.
Taylor Approximation: Taylor's Theorem, remainder term, Taylor series, standard examples, limits using Taylor series.
This module serves as an introduction to algebraic methods. These methods are central in modern mathematics and have found applications in many other sciences, but also in our everyday life. In this module, students will also gain an appreciation of the concept of proof in mathematics.
This module is a sequel to Algebraic Methods. It considers the abstract theory of linear spaces together with applications to matrix algebra and other areas of Mathematics (and its applications). Since linear spaces are of fundamental importance in almost every area of mathematics, the ideas and techniques discussed in this module lie at the heart of mathematics. Topics covered will include vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalisation, orthogonality and applications including conics.
Applications of logical reasoning, problem-solving strategies, and documentation of solutions. Introduction to mathematical resources for independent learning and research. Introduction to current subject developments. Attending 2 out 3 Undergraduate Mathematics Colloquia. Introduction to the computer algebra system Maple: elementary programming, use as a tool for solution of algebraic equations, calculus and linear algebra. Writing a CV and introduction to career development for mathematics graduates.
The concept of symmetry is one of the most fruitful ideas through which mankind has tried to understand order and beauty in nature and art. This module first develops the concept of symmetry in geometry. It subsequently discusses links with the fundamental notion of a group in algebra. Outline syllabus includes: Groups from geometry; Permutations; Basic group theory; Action of groups and applications to (i) isometries of regular polyhedra; (ii) counting colouring problems; Matrix groups.
In this module we will study linear partial differential equations, we will explore their properties and discuss the physical interpretation of certain equations and their solutions. We will learn how to solve first order equations using the method of characteristics and second order equations using the method of separation of variables.
Introduction to linear PDEs: Review of partial differentiation; first-order linear PDEs, the heat equation, Laplace's equation and the wave equation, with simple models that lead to these equations; the superposition principle; initial and boundary conditions
Separation of variables and series solutions: The method of separation of variables; simple separable solutions of the heat equation and Laplace’s equation; Fourier series; orthogonality of the Fourier basis; examples and interpretation of solutions
Solution by characteristics: the method of characteristics for first-order linear PDEs; examples and interpretation of solutions; characteristics of the wave equation; d’Alembert’s solution, with examples; domains of influence and dependence; causality.
This module builds on the Stage 1 Real Analysis 1 module. We will extend our knowledge of functions of one real variable, look at series, and study functions of several real variables and their derivatives.
The outline syllabus includes: Continuity and uniform continuity of functions of one variable, series and power series, the Riemann integral, limits and continuity for functions of several variables, differentiation of functions of several variables, extrema, the Inverse and Implicit Function Theorems.
Constructing suitable models for data is a key part of statistics. For example, we might want to model the yield of a chemical process in terms of the temperature and pressure of the process. Even if the temperature and pressure are fixed, there will be variation in the yield which motivates the use of a statistical model which includes a random component. In this module, students study linear regression models (including estimation from data and drawing of conclusions), the use of likelihood to estimate models and its application in simple stochastic models. Both theoretical and practical aspects are covered, including the use of R.
The main aim of this module is to give an introduction to the basics of differential geometry, keeping in mind the recent applications in mathematical physics and the analysis of pattern recognition. Outline syllabus includes: Curves and parameterization; Curvature of curves; Surfaces in Euclidean space; The first fundamental form; Curvature of surfaces; Geodesics.
This module will present a new perspective on Newton's familiar laws of motion. First we introduce variational calculus with applications such as finding the paths of shortest distance. This will lead us to the principle of least action from which we can derive Newton's law for conservative forces. We will also learn how symmetries lead to constants of motion. We then derive Hamilton's equations and discuss their underlying structures. The formalisms we introduce in this module form the basis for all of fundamental modern physics, from electromagnetism and general relativity, to the standard model of particle physics and string theory.
Review of Newton mechanics: Newton's law; harmonic and anharmonic oscillators (closed and unbound orbits, turning points); Kepler problem: energy and angular momentum conservation
Lagrangian Mechanics: Introdution to variational calculus with simple applications (shortest path - geodesic, soap film, brachistochrone problem); principle of least action: Euler-Lagrange equations (Newtonian mechanics with conservative forces); constraints and generalised coordinates (particle on a hoop, double pendulum, normal modes); Noether's theorem (energy and angular momentum conservation)
Hamiltonian Dynamics: Hamilton's equations; Legendre transform; Hamiltonian phase space (harmonic oscillator, anharmonic oscillators and the mathematical pendulum); Liouville's theorem; Poisson brackets.
Probability: Joint distributions of two or more discrete or continuous random variables. Marginal and conditional distributions. Independence. Properties of expectation, variance, covariance and correlation. Poisson process and its application. Sums of random variables with a random number of terms.
Transformations of random variables: Various methods for obtaining the distribution of a function of a random variable —method of distribution functions, method of transformations, method of generating functions. Method of transformations for several variables. Convolutions. Approximate method for transformations.
Sampling distributions: Sampling distributions related to the Normal distribution — distribution of sample mean and sample variance; independence of sample mean and variance; the t distribution in one- and two-sample problems.
Statistical inference: Basic ideas of inference — point and interval estimation, hypothesis testing.
Point estimation: Methods of comparing estimators — bias, variance, mean square error, consistency, efficiency. Method of moments estimation. The likelihood and log-likelihood functions. Maximum likelihood estimation.
Hypothesis testing: Basic ideas of hypothesis testing — null and alternative hypotheses; simple and composite hypotheses; one and two-sided alternatives; critical regions; types of error; size and power. Neyman-Pearson lemma. Simple null hypothesis versus composite alternative. Power functions. Locally and uniformly most powerful tests.
Composite null hypotheses. The maximum likelihood ratio test.
Interval estimation: Confidence limits and intervals. Intervals related to sampling from the Normal distribution. The method of pivotal functions. Confidence intervals based on the large sample distribution of the maximum likelihood estimator – Fisher information, Cramer-Rao lower bound. Relationship with hypothesis tests. Likelihood-based intervals.
This module is an introduction to the methods, tools and ideas of numerical computation. In mathematics, one often encounters standard problems for which there are no easily obtainable explicit solutions, given by a closed formula. Examples might be the task of determining the value of a particular integral, finding the roots of a certain non-linear equation or approximating the solution of a given differential equation. Different methods are presented for solving such problems on a modern computer, together with their applicability and error analysis. A significant part of the module is devoted to programming these methods and running them in MATLAB.
Introduction: Importance of numerical methods; short description of flops, round-off error, conditioning
Solution of linear and non-linear equations: bisection, Newton-Raphson, fixed point iteration
Interpolation and polynomial approximation: Taylor polynomials, Lagrange interpolation, divided differences, splines
Numerical integration: Newton-Cotes rules, Gaussian rules
Numerical differentiation: finite differences
Introduction to initial value problems for ODEs: Euler methods, trapezoidal method, Runge-Kutta methods.
This module introduces the basic ideas to solve certain ordinary differential equations, like first order scalar equations, second order linear equations and systems of linear equations. It mainly considers their qualitative and analytical aspects. Outline syllabus includes: First-order scalar ODEs; Second-order scalar linear ODEs; Existence and Uniqueness of Solutions; Autonomous systems of two linear first-order ODEs.
Can we square a circle? Can we trisect an angle? These two questions were studied by the Ancient Greeks and were only solved in the 19th century using algebraic structures such as rings, fields and polynomials. In this module, we introduce these ideas and concepts and show how they generalise well-known objects such as integers, rational numbers, prime numbers, etc. The theory is then applied to solve problems in Geometry and Number Theory. This part of algebra has many applications in electronic communication, in particular in coding theory and cryptography.
The security of our phone calls, bank transfers, etc. all rely on one area of Mathematics: Number Theory. This module is an elementary introduction to this wide area and focuses on solving Diophantine equations. In particular, we discuss (without proof) Fermat's Last Theorem, arguably one of the most spectacular mathematical achievements of the twentieth century. Outline syllabus includes: Modular Arithmetic; Prime Numbers; Introduction to Cryptography; Quadratic Residues; Diophantine Equations.
Revision of complex numbers, the complex plane, de Moivre's andEuler's theorems, roots of unity, triangle inequality
Sequencesand limits: Convergence of a sequence in the complex plane. Absoluteconvergence of complex series. Criteria for convergence. Power series, radiusof convergence
Complexfunctions: Domains, continuity, complex differentiation. Differentiation ofpower series. Complex exponential and logarithm, trigonometric, hyperbolicfunctions. Cauchy-Riemann equations
ComplexIntegration: Jordan curves, winding numbers. Cauchy's Theorem. Analytic functions.Liouville's Theorem, Maximum Modulus Theorem
Singularitiesof functions: poles, classification of singularities. Residues. Laurentexpansions. Applications of Cauchy's theorem. The residue theorem. Evaluationof real integrals.
Possibleadditional topics may include Rouche’s Theorem, other proofs of the FundamentalTheorem of Algebra, conformal mappings, Mobius mappings, elementary Riemannsurfaces, and harmonic functions.
There is no specific mathematical syllabus for this module.Students will study a topic in mathematics or statistics, either individuallyor within a small group, and produce an individual or group project on thetopic as well as individual coursework assignments. Projects will be chosenfrom published lists of individual and of group projects. The coursework andproject-work are supported by a series of workshops covering various forms ofwritten and oral communication and by supervision from an academic member ofstaff.
Theworkshops may include critically evaluating the following: a research articlein mathematics or statistics; a survey or magazine article aimed at ascientifically-literate but non-specialist audience; a mathematical biography;a poster presentation of a mathematical topic; a curriculum vitae; an oralpresentation with slides or board; a video or podcast on a mathematical topic.Guidance will be given on typesetting mathematics using LaTeX.”
The 2023/24 annual tuition fees for this course are:
- Home full-time £9,250
- EU full-time £13,500
- International full-time £18,000
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme, fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.*
Your fee status
The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.
Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.Search scholarships
Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
The Kent Scholarship for Academic Excellence
At Kent we recognise, encourage and reward excellence. We have created the Kent Scholarship for Academic Excellence.
The scholarship will be awarded to any applicant who achieves a minimum of A*AA over three A levels, or the equivalent qualifications (including BTEC and IB) as specified on our scholarships pages.
Teaching and assessment
Teaching amounts to approximately 16 hours of lectures and classes per week. Modules that involve programming or working with computer software packages usually include practical sessions.
The majority of Stage 1 modules are assessed by end-of-year examinations. Many Stage 2 and 3 modules include coursework which normally counts for 20% of the final assessment. Both Stage 2 and 3 marks count towards your final degree result.
For a student studying full time, each academic year of the programme will comprise 1200 learning hours which include both direct contact hours and private study hours. The precise breakdown of hours will be subject dependent and will vary according to modules. Please refer to the individual module details under Course Structure.
Methods of assessment will vary according to subject specialism and individual modules. Please refer to the individual module details under Course Structure.
The course aims to:
- Equip students with the technical appreciation, skills and knowledge appropriate to graduates in Mathematics.
Develop students’ facilities of rigorous reasoning and precise expression.
Develop students’ capabilities to formulate and solve mathematical problems.
Develop in students appreciation of recent developments in Mathematics, and of the links between the theory of Mathematics and its practical application.
Develop in students a logical, mathematical approach to solving problems.
Develop in students an enhanced capacity for independent thought and work.
Ensure students are competent in the use of information technology, and are familiar with computers, together with the relevant software.
Provide students with opportunities to study advanced topics in Mathematics, engage in research at some level, and develop communication and personal skills.
For the course involving a year in industry, to enable students to gain awareness of the application of technical concepts in the workplace.
Knowledge and understanding
You gain knowledge and understanding of:
- Core mathematical understanding in the principles of calculus, algebra, mathematical methods, discrete mathematics, analysis and linear algebra.
- Statistical understanding in the subjects of probability and inference.
- Information technology skills as relevant to mathematicians.
- Methods and techniques of mathematics.
- The role of logical mathematical argument and deductive reasoning.
You develop your intellectual skills in the following areas:
- Ability to demonstrate a reasonable understanding of the basic body of knowledge for Mathematics.
- Ability to demonstrate a reasonable level of skill in calculation and manipulation of the material written within the course and some capability to solve problems formulated within it.
- Ability to apply a range of core concepts and principles in well-defined contexts relevant to mathematics.
- Ability to use logical argument.
- Ability to demonstrate skill in solving mathematical problems by various appropriate methods.
- Ability in relevant computer skills and usage.
- Ability to work with relatively little guidance.
You gain subject-specific skills in the following areas:
- Ability to demonstrate knowledge of key mathematical concepts and topics, both explicitly and by applying them to the solution of problems.
- Ability to comprehend problems, abstract the essentials of problems and formulate them mathematically and in symbolic form so as to facilitate their analysis and solution.
- Ability to use computational and more general IT facilities as an aid to mathematical processes.
- Ability to present their mathematical arguments and the conclusions from them with clarity and accuracy.
You gain transferable skills in the following areas:
- Problem-solving skills, relating to qualitative and quantitative information.
- Communication skills, covering a variety of communication methods.
- Numeracy and computational skills.
- Information technology skills such as word-processing, internet communication, etc.
- Personal and interpersonal skills, work as a member of a team.
- Time-management and organisational skills, as evidenced by the ability to plan and implement efficient and effective modes of working.
- Study skills needed for continuing professional development.
Mathematics at Kent was ranked 19th for student satisfaction in The Complete University Guide 2023.
Recent graduates have gone on to work in:
- medical statistics
- the pharmaceutical industry
- the aerospace industry
- software development
- actuarial work
- civil service statistics
- chartered accountancy
- the oil industry.
Help finding a job
The University has a friendly Careers and Employability Service, which can give you advice on how to:
- apply for jobs
- write a good CV
- perform well in interviews.
You graduate with an excellent grounding in the fundamental concepts and principles of mathematics. Many career paths can benefit from the numerical and analytical skills you develop during your studies.
To help you appeal to employers, you also learn key transferable skills that are essential for all graduates. These include the ability to:
- think critically
- communicate your ideas and opinions
- manage your time effectively
- work independently or as part of a team.
You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.
This degree meets the educational requirements of the Chartered Mathematician designation, awarded by the Institute of Mathematics and its Applications (IMA),
when it is followed by subsequent training and experience in employment to obtain equivalent competencies to those specified by the Quality Assurance Agency (QAA) for taught master’s degrees.
Apply for Mathematics - BSc (Hons)
If you are from the UK or Ireland, you must apply for this course through UCAS. If you are not from the UK or Ireland, you can apply through UCAS or directly on our website if you have never used UCAS and you do not intend to use UCAS in the future.
Find out more about how to apply
United Kingdom/EU enquiries
Enquire online for full-time study
International student enquiries
T: +44 (0)1227 823254
Discover Uni information
Discover Uni is designed to support prospective students in deciding whether, where and what to study. The site replaces Unistats from September 2019.
Discover Uni is jointly owned by the Office for Students, the Department for the Economy Northern Ireland, the Higher Education Funding Council for Wales and the Scottish Funding Council.
- Information and guidance about higher education
- Information about courses
- Information about providers
Find out more about the Unistats dataset on the Higher Education Statistics Agency website.