Our modern world is heavily reliant on financial markets. Financial institutions depend on skilled individuals to manage their portfolios, applying mathematical modelling, statistical analysis and the problem-solving know-how of mathematics graduates.
The University will consider applications from students offering a wide range of qualifications. Typical requirements are listed below. Students offering alternative qualifications should contact us for further advice.
Please note that meeting this typical offer/minimum requirement does not guarantee an offer being made.Please also see our general entry requirements.
If you’ve taken exams under the new GCSE grading system, please see our conversion table to convert your GCSE grades.
ABC including an A in Mathematics. Use of Maths A level is not accepted as a required subject. Only one of General Studies or Critical Thinking can count as a third A level.
If taking both A level Mathematics and A level Further Mathematics:
ABD including Mathematics at grade A and Further Mathematics at grade B. Use of Maths A level is not accepted as a required subject. Only one of General Studies or Critical Thinking can count as a third A level.
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 16 points at HL including Mathematics 6 at HL
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.
Please see our English language entry requirements web page.
Please note that if you are required to meet an English language condition, 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 is designed for students who have not studied Microeconomics for Business before or who have not previously completed a comprehensive introductory course in economics. However, the content is such that it is also appropriate for students with A-level Economics or equivalent, as it focuses on the analysis, tools and knowledge of microeconomics for business.
The module applies economics to business issues and each topic is introduced assuming no previous knowledge of the subject. The lectures and related workshop programme explain the economic principles underlying the analysis of each topic and relate the theory to the real world and business examples. In particular, many examples are taken from the real world to show how economic analysis and models can be used to understand the different parts of business and how policy has been used to intervene in the working of the economy.
The module is carefully designed to tell you what topics are covered under each major subject area, to give readings for these subjects, and to provide a list of different types of questions to test and extend your understanding of the material.
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.
This module serves as an introduction to algebraic methods and linear algebra methods. These are central in modern mathematics, having found applications in many other sciences and also in our everyday life.
Indicative module content:
Basic set theory, Functions and Relations, Systems of linear equations and Gaussian elimination, Matrices and Determinants, Vector spaces and Linear Transformations, Diagonalisation, Orthogonality.
This module introduces widely-used mathematical methods for functions of a single variable. 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.
Complex numbers: Complex arithmetic, the complex conjugate, the Argand diagram, de Moivre's Theorem, modulus-argument form; elementary functions
Polynomials: Fundamental Theorem of Algebra (statement only), roots, factorization, rational functions, partial fractions
Single variable calculus: Differentiation, including product and chain rules; Fundamental Theorem of Calculus (statement only), elementary integrals, change of variables, integration by parts, differentiation of integrals with variable limits
Scalar ordinary differential equations (ODEs): definition; methods for first-order ODEs; principle of superposition for linear ODEs; particular integrals; second-order linear ODEs with constant coefficients; initial-value problems
Curve sketching: graphs of elementary functions, maxima, minima and points of inflection, asymptotes
This module introduces widely-used mathematical methods for vectors and functions of two or more variables. 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.
Vectors: Cartesian coordinates; vector algebra; scalar, vector and triple products (and geometric interpretation); straight lines and planes expressed as vector equations; parametrized curves; differentiation of vector-valued functions of a scalar variable; tangent vectors; vector fields (with everyday examples)
Partial differentiation: Functions of two variables; partial differentiation (including the chain rule and change of variables); maxima, minima and saddle points; Lagrange multipliers
Integration in two dimensions: Double integrals in Cartesian coordinates; plane polar coordinates; change of variables for double integrals; line integrals; Green's theorem (statement – justification on rectangular domains only)
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.
This is an introductory module to introduce students to the role and evolution of accounting
Topics to be covered may include: single entry accounting; double entry bookkeeping; financial reporting conventions; recording transactions and adjusting entries; principal financial statements; institutional requirements; auditing; monetary items; purchases and sales; bad and doubtful debts; inventory valuation; non-current assets and depreciation methods; liabilities; sole traders and clubs, partnerships, companies; capital structures; cash flow statements; interpretation of accounts through ratio analysis; problems of, and alternatives to, historical cost accounting.
This module is concerned with the principles which underlie the investment and financing decision making process. Before a rational decision can be made objectives need to be considered and models need to be built. Short-term decisions are dealt with first, together with relevant costs. One such cost is the time value of money. This leads to long term investment decisions which are examined using the economic theory of choice, first assuming perfect capital markets and certainty. These assumptions are then relaxed so that such problems as incorporating capital rationing and risk into the investment decision are fully considered. The module proceeds by looking at the financing decision. The financial system within which business organisations operate is examined, followed by the specific sources and costs of long and short-term capital, including the management of fixed and working capital
Macroeconomics for business offers the possibility of analysing economic activity in a national economy and its interrelationships. Emphasis is on understanding the important questions in determination of level of national output, aggregate spending and fiscal policy, money supply and financial crisis, determinants of economic growth and relevant economic policies. The module explains the role of economic policies in addressing economic problems such as unemployment and inflation. Theoretical concepts are illustrated from a range of UK economy and international applications.
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.
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.
Formulation/Mathematical modelling of optimisation problems
Linear Optimisation: Graphical method, Simplex method, Phase I method, Dual problems,
Non-linear Optimisation: Unconstrained one dimensional problems, Unconstrained high dimensional problems, Constrained optimisation.
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.
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.
This module covers aspects of Statistics which are particularly relevant to insurance. Some topics (such as risk theory and credibility theory) have been developed specifically for actuarial use. Other areas (such as Bayesian Statistics) have been developed in other contexts but now find applications in actuarial fields. Stochastic processes of events such as accidents, together with the financial flow of their payouts underpin much of the work. Since the earliest games of chance, the probability of ruin has been a topic of interest. Outline Syllabus includes: Decision Theory; Bayesian Statistics; Loss Distributions; Reinsurance; Credibility Theory; Empirical Bayes Credibility theory; Risk Models; Ruin Theory; Generalised Linear Models; Run-off Triangles.
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, we study how suitable models can be constructed, how to fit them to data and how suitable conclusions can be drawn. Both theoretical and practical aspects are covered, including the use of R.
You can choose to spend a year working in industry between Stages 2 and 3. We can offer help and advice in finding a placement.
Spending a year in industry greatly enhances your CV and gives you the opportunity to put your academic skills into practice. It also gives you an idea of possible career options. Recent placements have included IBM, management consultancies, government departments, actuarial firms and banks.
The module will aim to cover the following topics:
• the conceptual framework of financial reporting
• the financial reporting environment
• the regulation of financial reporting
• group accounting
• the International Accounting Standards Board
• content and application of International Accounting Standards as appropriate
• accounting standards
• accounting for transactions in financial statements
A synopsis of the curriculum
The module will aim to cover the following topics:
• The UK tax system including the overall function and purpose of taxation in a modern economy, different types of taxes, principal sources of revenue law and practice, tax avoidance and tax evasion.
• Income tax liabilities including the scope of income tax, income from employment and self-employment, property and investment income, the computation of taxable income and income tax liability, the use of exemptions and reliefs in deferring and minimising income tax liabilities.
• Corporation tax liabilities including the scope of corporation tax, profits chargeable to corporation tax, the computation of corporation tax liability, the use of exemptions and reliefs in deferring and minimising corporation tax liabilities.
• Chargeable gains including the scope of taxation of capital gains, the basic principles of computing gains and losses, gains and losses on the disposal of movable and immovable property, gains and losses on the disposal of shares and securities, the computation of capital gains tax payable by individuals, the use of exemptions and reliefs in deferring and minimising tax liabilities arising on the disposal of capital assets.
• National insurance contributions including the scope of national insurance, class 1 and 1A contributions for employed persons, class 2 and 4 contributions for self-employed persons.
• Value added tax including the scope of VAT, registration requirements, computation of VAT liabilities.
• Inheritance tax and the use of exemptions and reliefs in deferring and minimising inheritance tax liabilities. Introduction to international tax strategy, implementation, compliance and defence. An understanding of principles of normative ethics in business and in taxation from local and global perspectives.
• The obligations of taxpayers and/or their agents including the systems for self-assessment and the making of returns, the time limits for the submission of information, claims and payment of tax, the procedures relating to enquiries, appeals and disputes, penalties for non-compliance.
This module is concerned with International Investment Banks’ products and strategies that involve the description and analyses of the characteristics of more commonly used financial derivative instruments such as forward and future contracts, swaps, and options involving commodities, interest, and equities markets. Modern financial techniques are used to value financial derivatives. The main emphasis of the module is on how International Investment Banks value, replicate, and arbitrage the financial instruments and how they encourage their clients to use derivative products to implement risk management strategies in the context of corporate applications.
In particular, students will first cover the topics related to forward, futures and swap contracts. They will then be introduced to options and various strategies thereof. Valuing options using Black-Scholes model and binomial trees is also an important part of the module. The important finance concepts of no-arbitrage and risk-neutral valuation and their implications for pricing financial derivatives are also covered in the module. This will help students to learn the techniques used in valuing financial derivatives and hedging risk exposure.
Successful completion of the module will provide a solid base for the student wishing to pursue a career in International Investment Banking and Treasury Management. The students will have the knowledge of essential techniques of risk management and financial derivative trading.
This module begins with a focus on the financial system of the UK, including the major players in the markets and key interrelations. It then proceeds to cover key topics, including: advanced portfolio theory, the capital asset pricing model, arbitrage pricing theory, the implications and empirical evidence relating to the efficient market hypothesis, capital structure and the cost of capital in a taxation environment, interaction of investment and financing decisions, decomposition of risk, options and pricing, risk management, dividends and dividend valuation models, mergers and failures and evaluating financial strategies.
This module will cover the following topics:
• The historical development of auditing
• The nature, importance, objectives and underlying theory of auditing
• The philosophy, concepts and basic postulates of auditing
• The regulatory and socio-economic environment within which auditing process takes place
• Auditing implications of agency theories of the firm
• Auditing implications of the efficient markets hypothesis
• The statutory and contractual bases of auditing, including auditing regulation and auditors' legal duties and liabilities
• Truth and fairness in financial reporting
• Materiality and audit judgement
• Audit independence
• The nature and causes of the audit expectation gap
• Auditors' professional ethics and standards
• Audit quality control, planning, programming, performance, supervision and review
• The nature and types of audit evidence
• Principles of internal control
• Systems based auditing and the nature and relationship of compliance and substantive testing
• The audit risk model and statistical sampling
• Audit procedures for major classes of assets, liabilities, income and expenditure
• Audit reporting.
Discrete mathematics has found new applications in the encoding of information. Online banking requires the encoding of information to protect it from eavesdroppers. Digital television signals are subject to distortion by noise, so information must be encoded in a way that allows for the correction of this noise contamination. Different methods are used to encode information in these scenarios, but they are each based on results in abstract algebra. This module will provide a self-contained introduction to this general area of mathematics.
Syllabus: Modular arithmetic, polynomials and finite fields. Applications to
• orthogonal Latin squares,
• cryptography, including introduction to classical ciphers and public key ciphers such as RSA,
• "coin-tossing over a telephone",
• linear feedback shift registers and m-sequences,
• cyclic codes including Hamming,
Most differential equations which arise from physical systems cannot be solved explicitly in closed form, and thus numerical solutions are an invaluable way to obtain information about the underlying physical system. The first half of the module is concerned with ordinary differential equations. Several different numerical methods are introduced and error growth is studied. Both initial value and boundary value problems are investigated. The second half of the module deals with the numerical solution of partial differential equations. The syllabus includes: initial value problems for ordinary differential equations; Taylor methods; Runge-Kutta methods; multistep methods; error bounds and stability; boundary value problems for ordinary differential equations; finite difference schemes; difference schemes for partial differential equations; iterative methods; stability analysis.
Introduction: Principles and examples of stochastic modelling, types of stochastic process, Markov property and Markov processes, short-term and long-run properties. Applications in various research areas.
Random walks: The simple random walk. Walk with two absorbing barriers. First–step decomposition technique. Probabilities of absorption. Duration of walk. Application of results to other simple random walks. General random walks. Applications.
Discrete time Markov chains: n–step transition probabilities. Chapman-Kolmogorov equations. Classification of states. Equilibrium and stationary distribution. Mean recurrence times. Simple estimation of transition probabilities. Time inhomogeneous chains. Elementary renewal theory. Simulations. Applications.
Continuous time Markov chains: Transition probability functions. Generator matrix. Kolmogorov forward and backward equations. Poisson process. Birth and death processes. Time inhomogeneous chains. Renewal processes. Applications.
Queues and branching processes: Properties of queues - arrivals, service time, length of the queue, waiting times, busy periods. The single-server queue and its stationary behaviour. Queues with several servers. Branching processes. Applications.
Marks on this module can count towards exemption from the professional examination CT4 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.
A time series is a collection of observations made sequentially in time. Examples occur in a variety of fields, ranging from economics to engineering, and methods of analysing time series constitute an important area of statistics. This module focuses initially on various time series models, including some recent developments, and provides modern statistical tools for their analysis. The second part of the module covers extensively simulation methods. These methods are becoming increasingly important tools as simulation models can be easily designed and run on modern PCs. Various practical examples are considered to help students tackle the analysis of real data.The syllabus includes: Difference equations, Stationary Time Series: ARMA process. Nonstationary Processes: ARIMA Model Building and Testing: Estimation, Box Jenkins, Criteria for choosing between models, Diagnostic tests.Forecasting: Box-Jenkins, Prediction bounds. Testing for Trends and Unit Roots: Dickey-Fuller, ADF, Structural change, Trend-stationarity vs difference stationarity. Seasonality and Volatility: ARCH, GARCH, ML estimation. Multiequation Time Series Models: Spectral Analysis. Generation of pseudo – random numbers, simulation methods: inverse transform and acceptance-rejection, design issues and sensitivity analysis.
Marks on this module can count towards exemption from the professional examination CT6 of the Institute and Faculty of Actuaries. Please see http://www.kent.ac.uk/casri/Accreditation/index.html for further details.
There is no specific mathematical syllabus for this module; students will chose a topic in mathematics, statistics or financial mathematics from a published list on which to base their coursework assessments (different topics for levels 6 and 7). The coursework is supported by a series of workshops covering various forms of written and oral communication. These may include critically evaluating the following: a research article in mathematics, statistics or finance; a survey or magazine article aimed at a scientifically-literate but non-specialist audience; a mathematical biography; a poster presentation of a mathematical topic; a curriculum vitae; an oral presentation with slides or board; a video or podcast on a mathematical topic. Guidance will be given on typesetting mathematics using LaTeX.
In this module we study the fundamental concepts and results in game theory. We start by analysing combinatorial games, and discuss game trees, winning strategies, and the classification of positions in so called impartial combinatorial games. We then move on to discuss two-player zero-sum games and introduce security levels, pure and mixed strategies, and prove the famous von Neumann Minimax Theorem. We will see how to solve zero-sum two player games using domination and discuss a general method based on linear programming. Subsequently we analyse arbitrary sum two-player games and discuss utility, best responses, Nash equilibria, and the Nash Equilibrium Theorem. The final part of the module is devoted to multi-player games and cooperation; we analyse coalitions, the core of the game, and the Shapley value.
Sampling: Simple random sampling. Sampling for proportions and percentages. Estimation of sample size. Stratified sampling. Systematic sampling. Ratio and regression estimates. Cluster sampling. Multi-stage sampling and design effect. Questionnaire design. Response bias and non-response.
General principles of experimental design: blocking, randomization, replication. One-way ANOVA. Two-way ANOVA. Orthogonal and non-orthogonal designs. Factorial designs: confounding, fractional replication. Analysis of covariance.
Design of clinical trials: blinding, placebos, eligibility, ethics, data monitoring and interim analysis. Good clinical practice, the statistical analysis plan, the protocol. Equivalence and noninferiority.
Multivariate normal distribution, Inference from multivariate normal samples, principal component analysis, mixture models, factor analysis, clustering methods, discrimination and classification, graphical models, the use of appropriate software.
Linear PDEs. Dispersion relations. Review of d'Alembert’s solutions of the wave equation. Review of Fourier transforms for solving linear diffusion equations.
Quasi-linear first-order PDEs. Total differential equations. Integral curves and integrability conditions. The method of characteristics.
Shock waves. Discontinuous solutions. Breaking time. Rankine-Hugoniot jump condition. Shock waves. Rarefaction waves. Applications of shock waves, including traffic flow.
General first-order nonlinear PDEs. Charpit's method, Monge Cone, the complete integral.
Nonlinear PDEs. Burgers' equation; the Cole-Hopf transformation and exact solutions. Travelling wave and scaling solutions of nonlinear PDEs. Applications of travelling wave and scaling solutions to reaction-diffusion equations. Exact solutions of nonlinear PDEs. Applications of nonlinear waves, including to ocean waves (e.g. rogue waves, tsunamis).
Statistics methods contribute significantly to areas such as biology, ecology, sociology and economics. The real data collected does not always follow standard statistical models. This module looks at modern statistical models and methods that can be utilised for such data, making use of R programs to execute these methods.
Indicative module content: Motivating examples; model fitting through maximum likelihood for specific examples; function optimization methods; profile likelihood; score tests; Wald tests; confidence interval construction; latent variable models; EM algorithm; mixture models; simulation methods; importance sampling; kernel density estimation; Monte Carlo inference; bootstrap; permutation tests; R programs.
The 2020/21 annual tuition fees for this programme are:
For details of when and how to pay fees and charges, please see our Student Finance Guide.
Full-time tuition fees for Home and EU undergraduates are £9,250.
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.*
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.
Full-time tuition fees for Home and EU undergraduates are £1,385.
Full-time tuition fees for Home and EU undergraduates are £1,385.
Students studying abroad for less than one academic year will pay full fees according to their fee status.
Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
You may be eligible for government finance to help pay for the costs of studying. See the Government's student finance website.
Scholarships are available for excellence in academic performance, sport and music and are awarded on merit. For further information on the range of awards available and to make an application see our scholarships website.
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 AAA over three A levels, or the equivalent qualifications (including BTEC and IB) as specified on our scholarships pages.
The scholarship is also extended to those who achieve AAB at A level (or specified equivalents) where one of the subjects is either mathematics or a modern foreign language. Please review the eligibility criteria.
Teaching is by a combination of lectures and seminars. Modules that involve programming or working with computer software packages usually include practical sessions.
Assessment is by a combination of coursework and examination. 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.
For programme aims to:
You gain knowledge and understanding of:
You develop your intellectual skills in the following areas:
You gain subject-specific skills in the following areas:
You gain transferable skills in the following areas:
All University of Kent courses are regulated by the Office for Students.
Based on the evidence available, the TEF Panel judged that the University of Kent delivers consistently outstanding teaching, learning and outcomes for its students. It is of the highest quality found in the UK.
Please see the University of Kent's Statement of Findings for more information.
Mathematics at Kent scored 91% overall in The Complete University Guide 2021.
For graduate prospects, Mathematics at Kent was ranked 3rd in The Complete University Guide 2021, and scored over 89% in The Times Good University Guide 2020 and 87% in The Guardian University Guide 2020.
Over 95% of Mathematics and Statistics graduates who responded to the most recent national survey of graduate destinations were in work or further study within six months (DLHE, 2017).
Recent graduates have gone into careers in accountancy training with firms such as KPMG and Ernst & Young, medical statistics, the pharmaceutical industry, the aerospace industry, software development, teaching, actuarial work, Civil Service statistics, chartered accountancy, the oil industry and postgraduate research.
You acquire many transferable skills including the ability to deal with challenging ideas, to think critically, to write well and to present your ideas clearly, all of which are considered essential by graduate employers.
The degree provides various exemptions from the examinations of the Institute of Chartered Accountants.
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