Mathematics is important to the modern world. All quantitative science, including both physical and social sciences, is based on it. It provides the theoretical framework for physical science, statistics and data analysis as well as computer science. Our programme reflects this diversity and the excitement generated by new discoveries within mathematics.

## Overview

Our Mathematics and Statistics programmes combine the in-house expertise of our internationally-renowned mathematicians and statisticians to ensure you are fully prepared for your future career. You will be encouraged to fulfil your potential whilst studying in our friendly and dynamic school based in the multi-award-winning Sibson Building.

### Our degree programme

To help bridge the gap between school and university, you’ll attend small group tutorials in Stage 1, where you can practice the new mathematics you’ll be learning, ask questions and work with other students to find solutions. You’ll study a mixture of pure and applied mathematics, and statistics, providing you with a solid foundation for your later studies.

In Stage 2, you study some core modules which build upon the material learnt at Stage 1. You also start to tailor your degree to your interests through our range of optional modules, continuing to explore the areas you enjoy into Stage 3.

#### Accreditation

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.

#### Student view

Kezia shares her experiences studying BSc Mathematics and Statistics at Kent.

### Year in industry

If you want to gain paid industry experience as part of your degree programme, our popular Mathematics and Statistics with a Year in Industry. If you decide to take the Year in Industry, our in-house Placements Team will support you in developing the skills and knowledge needed to successfully secure a placement through a specialist programme of workshops and events.

### Foundation year

If your grades do not qualify you for direct entry to this programme, you may be able to take a four-year degree with a foundation year. For more details see Mathematics including a Foundation Year.

### Study resources

You have access to a range of professional mathematical and statistical software such as:

- Maple
- MATLAB
- Minitab.

Our staff use these packages in their teaching and research.

### Extra activities

The School of Mathematics and Actuarial Science Student Society is run by students. It aims to improve the student experience for its members, socially and academically. In previous years the Society has organised:

- talks and workshops
- extra revision sessions
- socials and networking events.
- seminars and workshops employability events.

The School of Mathematics, Statistics and Actuarial Science also puts on regular events that you are welcome to attend. In the past, these have included:

- seminars and workshops
- employability events.

#### Independent rankings

Mathematics at Kent scored 91.5 out of 100 in *The Complete University Guide 2019*.

In the National Student Survey 2018, over 87% of final-year Mathematics and Statistics students who completed the survey, were satisfied with the overall quality of their course.

Of Mathematics and Statistics students who graduated from Kent in 2017 and completed a national survey, over 95% were in work or further study within six months (DLHE).

## Teaching Excellence Framework

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.

## Course structure

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.

### Stage 1

Compulsory modules currently include | Credits |
---|---|

MA306 - Statistics
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. View full module details |
15 |

MA343 - Algebraic Methods
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. View full module details |
15 |

MA344 - Applications of Mathematics
This module introduces mathematical modelling and Newtonian mechanics. Tutorials and Maple worksheets will be used to support taught material. The modelling cycle: General description with examples; Newton's law of cooling; population growth (Malthusian and logistic models); simple reaction kinetics (unimolecular and bimolecular reactions); dimensional consistency Motion of a body: frames of reference; a particle's position vector and its time derivatives (velocity and acceleration) in Cartesian coordinates; mass, momentum and centre of mass; Newton's laws of motion; linear springs; gravitational acceleration and the pendulum; projectile motion Orbital motion: Newton's law of gravitation; position, velocity and acceleration in plane polar coordinates; planetary motion and Kepler's laws. View full module details |
15 |

MA346 - Linear Algebra
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. View full module details |
15 |

MA348 - Mathematical Methods 1
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 View full module details |
15 |

MA349 - Mathematical Methods 2
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) View full module details |
15 |

MA351 - Probability
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. View full module details |
15 |

MA352 - Real Analysis 1
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. View full module details |
15 |

### Stage 2

Compulsory modules currently include | Credits |
---|---|

MA5501 - Applied Statistical Modelling 1
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. View full module details |
15 |

MA5503 - Groups and Symmetries
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. View full module details |
15 |

MA5505 - Linear Partial Differential Equations
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. View full module details |
15 |

MA5507 - Mathematical Statistics
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. View full module details |
15 |

MA5513 - Real Analysis 2
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. View full module details |
15 |

Optional modules may include | Credits |
---|---|

MA5514 - Rings and Fields
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. View full module details |
15 |

MA5512 - Ordinary Differential Equations
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. View full module details |
15 |

MA5504 - Lagrangian and Hamiltonian Dynamics
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. Indicative syllabus: 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. View full module details |
15 |

MA5502 - Curves and Surfaces
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. View full module details |
15 |

MA501 - Statistics for Insurance
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. View full module details |
15 |

### Stage 3

Compulsory modules currently include | Credits |
---|---|

MA6528 - Principles of Data Collection
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. View full module details |
15 |

MA6512 - Applied Statistical Modelling 2
This is a practical module to develop the skills required by a professional statistician (report writing, consultancy, presentation, wider appreciation of assumptions underlying methods, selection and application of analysis method, researching methods). Software: R, SPSS and Excel (where appropriate/possible). Report writing in Word. PowerPoint for presentations. • Presentation of data • Report writing and presentation skills • Hypothesis testing: formulating questions, converting to hypotheses, parametric and non-parametric methods and their assumptions, selection of appropriate method, application and reporting. Use of resources to explore and apply additional tests. Parametric and non-parametric tests include, but are not limited to, t-tests, likelihood ratio tests, score tests, Wald test, chi-squared tests, Mann Whitney U-test, Wilcoxon signed rank test, McNemar's test. • Linear and Generalised Linear Models: simple linear and multiple regression, ANOVA and ANCOVA, understanding the limitations of linear regression, generalised linear models, selecting the appropriate distribution for the data set, understanding the difference between fixed and random effects, fitting models with random effects, model selection. • Consultancy skills: group work exercise(s) View full module details |
15 |

Optional modules may include | Credits |
---|---|

MA771 - Computational Statistics
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. View full module details |
15 |

MA6529 - Statistical Learning
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. View full module details |
15 |

MA636 - Stochastic Processes
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. View full module details |
15 |

MA6510 - Advances in Statistics
Each year three topics will be offered and will reflect recent advances in statistical modelling and statistical methodology. Example topics are: a) Statistical Ecology: Understanding demographic parameters and how they are used to model population dynamics. Estimating abundance and the effect of heterogeneity. Models for estimating survival probabilities. Multi-site and multi-state models. Classical model-selection. Complex models. Case studies. b) Survival analysis: Survival data, types of censoring. Failure times and hazard functions; Accelerated failure time model. Parametric models, exponential, piecewise exponential, Weibull. Nonparametric estimates: the Kaplan-Meier estimator, and asymptotic confidence regions. Parametric inference. Survival data with covariates. Proportional hazards. Cox's model and inference. Computer software: R and WinBUGS. c) Regression models with many variables: Examples of high-dimensional problems; Penalized maximum likelihood; Ridge regression; non-negative garrote; Lasso and adaptive Lasso estimation; LARS algorithm; Oracle property; Elastic Net; Group lasso. d) Modern nonparametric statistics: Bias-variance trade-off, Kernel density estimation, Kernel smoothing, Locally linear and locally quadratic estimation, basis function methods. View full module details |
15 |

MA639 - Time Series Modelling and Simulation
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. View full module details |
15 |

MA538 - Applied Bayesian Modelling
The origins of Bayesian inference lie in Bayes' Theorem for density functions; the likelihood function and the prior distribution combine to provide a posterior distribution which reflects beliefs about an unknown parameter based on the data and prior beliefs. Statistical inference is determined solely by the posterior distribution. So, for example, an estimate of the parameter could be the mean value of the posterior distribution. This module will provide a full description of Bayesian analysis and cover popular models, such as the normal distribution. Initially, the flavour will be one of describing the Bayesian counterparts to well known classical procedures such as hypothesis testing and confidence intervals. Outline Syllabus includes: Bayes Theorem for density functions; Exchangeability; Choice of priors; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs samplers; OpenBUGS; Bayesian hierarchical models; Applications of hierarchical models; Bayesian model choice. View full module details |
15 |

MA6503 - Communicating Mathematics
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. View full module details |
15 |

MA6504 - Discovering and Communicating Mathematics
There is no specific mathematical syllabus for this module. Students will study a topic in mathematics or statistics, either individually or within a small group, and produce an individual or group project on the topic as well as individual coursework assignments. Projects will be chosen from published lists of individual and of group projects. The coursework and project-work are supported by a series of workshops covering various forms of written and oral communication and by supervision from an academic member of staff. The workshops may include critically evaluating the following: a research article in mathematics or statistics; 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. View full module details |
30 |

MA567 - Topology
This module is an introduction to point-set topology, a topic that is relevant to many other areas of mathematics. In it, we will be looking at the concept of topological spaces and related constructions. In an Euclidean space, an "open set" is defined as a (possibly infinite) union of open "epsilon-balls". A topological space generalises the notion of "open set" axiomatically, leading to some interesting and sometimes surprising geometric consequences. For example, we will encounter spaces where every sequence of points converges to every point in the space, see why for topologists a doughnut is the same as a coffee cup, and have a look at famous objects such as the Moebius strip or the Klein bottle. View full module details |
15 |

MA574 - Polynomials in Several Variables
This module provides a rigorous foundation for the solution of systems of polynomial equations in many variables. In the 1890s, David Hilbert proved four ground-breaking theorems that prepared the way for Emmy Nöther's famous foundational work in the 1920s on ring theory and ideals in abstract algebra. This module will echo that historical progress, developing Hilbert's theorems and the essential canon of ring theory in the context of polynomial rings. It will take a modern perspective on the subject, using the Gröbner bases developed in the 1960s together with ideas of computer algebra pioneered in the 1980s. The syllabus will include • Multivariate polynomials, monomial orders, division algorithm, Gröbner bases; • Hilbert's Nullstellensatz and its meaning and consequences for solving polynomials in several variables; • Elimination theory and applications; • Linear equations over systems of polynomials, syzygies. View full module details |
15 |

MA595 - Graphs and Combinatorics
Combinatorics is a field in mathematics that studies discrete, usually finite, structures, such as graphs. It not only plays an important role in numerous parts of mathematics, but also has real world applications. In particular, it underpins a variety of computational processes used in digital technologies and the design of computing hardware. Among other things, this module provides an introduction to graph theory. Graphs are discrete objects consisting of vertices that are connected by edges. We will discuss a variety of concepts and results in graph theory, and some fundamental graph algorithms. Topics may include, but are not restricted to: trees, shortest paths problems, walks on graphs, graph colourings and embeddings, flows and matchings, and matrices and graphs. View full module details |
15 |

MA6544 - Nonlinear Systems and Applications
• Scalar autonomous nonlinear first-order ODEs. Review of steady states and their stability; the slope fields and phase lines. • Autonomous systems of two nonlinear first-order ODEs. The phase plane; Equilibra and nullclines; Linearisation about equilibra; Stability analysis; Constructing phase portraits; Applications. Nondimensionalisation. • Stability, instability and limit cycles. Liapunov functions and Liapunov's theorem; periodic solutions and limit cycles; Bendixson's Negative Criterion; The Dulac criterion; the Poincare-Bendixson theorem; Examples. • Dynamics of first order difference equations. Linear first order difference equations; Simple models and cobwebbing: a graphical procedure of solution; Equilibrium points and their stability; Periodic solutions and cycles. The discrete logistic model and bifurcations. View full module details |
15 |

MA6591 - Mathematics in the World of Finance
This module provides an overview of analytical careers in finance and explores the mathematical techniques used by actuaries, accountants and financial analysts. Students will learn about different types of financials assets, such as shares, bonds and derivatives and how to work out how much they are worth. They will also look at different types of debt and learn how mortgages and other loans are calculated. Developing these themes, the module will explain how to use maths to make financial decisions, such as how much an investor should pay for a financial asset or how a company can decide which projects to invest in or how much money to borrow. Risk management is a vital part of most mathematical careers in finance so the module will also cover different mathematical techniques for measuring and mitigating financial risk. Extension topics may include complex derivatives, economic theories of finance and the dangers of misusing mathematics. The module provides an opportunity to apply complex mathematical techniques to important real-world questions and is excellent preparation for those considering a financial career. Introduction to financial mathematics: Key uses of mathematics in finance; key practitioners of financial mathematics. Financial valuation and cash flow analysis: Discounting, Interest rates and time requirements, Future and Present value. Project Evaluation. Characteristics and valuation of different financial securities: Debt capital, bonds and stocks, valuation of bonds and stocks. Loans and interest rates: term structure of interest rates, spot and forward rates, types of loan, APR, loan schedules. Capital structure and the cost of capital: Gearing, WACC, understanding betas. Additional topics that may be covered: arbitrage and forward contracts, efficient markets hypothesis, pricing and valuing forward contracts, option pricing and the Black Scholes model, credit derivatives and systemic risks, limitations of mathematical modelling. View full module details |
15 |

MA691 - Linear and Nonlinear Waves
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). View full module details |
15 |

MA6517 - Functions of a Complex Variable
Revision of complex numbers, the complex plane, de Moivre's and Euler's theorems, roots of unity, triangle inequality Sequences and limits: Convergence of a sequence in the complex plane. Absolute convergence of complex series. Criteria for convergence. Power series, radius of convergence Complex functions: Domains, continuity, complex differentiation. Differentiation of power series. Complex exponential and logarithm, trigonometric, hyperbolic functions. Cauchy-Riemann equations Complex Integration: Jordan curves, winding numbers. Cauchy's Theorem. Analytic functions. Liouville's Theorem, Maximum Modulus Theorem Singularities of functions: poles, classification of singularities. Residues. Laurent expansions. Applications of Cauchy's theorem. The residue theorem. Evaluation of real integrals. Possible additional topics may include Rouche’s Theorem, other proofs of the Fundamental Theorem of Algebra, conformal mappings, Mobius mappings, elementary Riemann surfaces, and harmonic functions. View full module details |
15 |

MA6518 - Games and Strategy
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. View full module details |
15 |

## 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.

### Contact Hours

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.

### Programme aims

The programme aims to:

- equip students with the technical appreciation, skills and knowledge appropriate to a degree in mathematics and statistics
- develop students’ facilities of rigorous reasoning and precise expression
- develop students’ abilities to formulate and solve mathematical problems
- encourage an appreciation of recent developments in mathematics and statistics and of the links between theory and practical applications
- provide students with a logical, mathematical approach to solving problems
- provide students with an enhanced capacity for independent thought and work
- ensure students are competent in the use of information technology and are familiar with computers and the relevant software
- provide students with opportunities to study advanced topics in mathematics and statistics, engage in research at some level, and develop communication and personal skills
- provide successful students with eligibility for certain exemptions from examinations of the Royal Statistical Society

### Learning outcomes

#### Knowledge and understanding

You gain knowledge and understanding of:

- the core principles of calculus, algebra, mathematical methods, discrete mathematics, analysis and linear algebra
- statistics in the areas of probability and inference
- information technology as relevant to mathematicians
- methods and techniques of mathematics and statistics
- the role of logical mathematical argument and deductive reasoning.

#### Intellectual skills

You develop your intellectual skills in the following areas:

- the ability to demonstrate a reasonable understanding of mathematics and statistics
- the calculation and manipulation of the material written within the programme
- the ability to apply a range of concepts and principles in various contexts
- the ability to use logical argument
- the ability to solve mathematical and statistical problems by various methods
- the relevant computer skills
- the ability to work independently.

#### Subject-specific skills

You gain subject-specific skills in the following areas:

- the ability to demonstrate knowledge of key mathematical and statistical concepts and topics, both explicitly and by applying them to the solution of problems
- the 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
- the use of computational and more general IT facilities as an aid to mathematical and statistical processes
- the presentation of mathematical and statistical arguments and conclusions with clarity and accuracy.

#### Transferable skills

You gain transferable skills in the following areas:

- problem-solving skills, relating to qualitative and quantitative information
- communication skills
- numeracy and computational skills
- information-retrieval skills, in relation to primary and secondary information sources, including through online computer searches
- information technology skills such as word-processing, spreadsheet use and internet communication
- time-management and organisational skills, as shown by the ability to plan and implement effective modes of working
- study skills needed for continuing professional development.

## Careers

Through your studies, 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.

Recent graduates have gone into careers in medical statistics, the pharmaceutical industry, the aerospace industry, software development, teaching, actuarial work, Civil Service statistics, chartered accountancy, the oil industry and postgraduate research.

### Professional recognition

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.

Maths is a much broader subject than you would think. It’s only when you get to university that you realise how many different topics you can study.

Andrew Paul Mathematics with a Year in Industry BSc

## Entry requirements

### Home/EU students

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.

It is not possible to offer places to all students who meet this typical offer/minimum requirement.

### New GCSE grades

If you’ve taken exams under the new GCSE grading system, please see our conversion table to convert your GCSE grades.

Qualification | Typical offer/minimum requirement |
---|---|

A level | ABB 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: ABC including Mathematics at grade A and Further Mathematics at grade C. 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. |

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. |

BTEC Level 3 Extended Diploma (formerly BTEC National Diploma) | 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. |

International Baccalaureate | 34 points overall or 17 points at HL with Mathematics 6 at HL |

### International students

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 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.

### General entry requirements

Please also see our general entry requirements.

## Fees

The 2020/21 tuition fees have not yet been set. As a guide only, the 2019/20 annual tuition fees for this programme are:

UK/EU | Overseas | |
---|---|---|

Full-time |
£9250 | £15700 |

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.

### General additional costs

Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.

## Funding

#### University funding

Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.

#### Government funding

You may be eligible for government finance to help pay for the costs of studying. See the Government's student finance website.

### Scholarships

#### General scholarships

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.

#### 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 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.