School of Mathematics, Statistics & Actuarial Science

Statistics - MSc

Canterbury

This programme, accredited by the Royal Statistical Society (RSS), is excellent preparation for careers in any field requiring a strong statistical background. It equips aspiring professional statisticians with the skills they will need for posts in industry, government, research and teaching.

The statistics group at SMSAS is highly qualified to lead your advanced study in statistics. Statistical research is thriving here and this research work informs our MSc teaching, through lectures, practical work and projects.

Logo of the Royal Statistical Society

Accredited by the RSS.

Overview

The MSc in Statistics is accredited by the Royal Statistical Society (RSS) and is excellent preparation for careers in any field requiring a strong statistical background.

The programme, which has recently been updated, trains professional statisticians for posts in industry, government, research and teaching. It provides a suitable preparation for careers in other fields requiring a strong statistical background. Core modules give a thorough grounding in modern statistical methods and there is the opportunity to choose additional topics to study.

Statistics at Kent provides:

  • a programme that gives you the opportunity to develop practical, mathematical and computing skills in statistics, while working on challenging and important problems relevant to a broad range of potential employers
  • teaching and supervision by staff who are research-active, with established reputations and who are accessible, supportive and genuinely interested in your work
  • advanced and accessible computing and other facilities
  • a congenial work atmosphere with pleasant surroundings, where you can socialise and discuss issues with a community of other students.

About the School of Mathematics, Statistics and Actuarial Science (SMSAS)

The School has a strong reputation for world-class research and a well-established system of support and training, with a high level of contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School. We encourage all postgraduate statistics students to take part in statistics seminars and to help in tutorial classes.

The Statistics Group is forward-thinking, with varied research, and received high rankings in the Research Excellence Framework (REF) 2014 for research power and quality.

National ratings

In the Research Excellence Framework (REF) 2014, research by the School of Mathematics, Statistics and Actuarial Science was ranked 25th in the UK for research power and 100% or our research was judged to be of international quality.

An impressive 92% of our research-active staff submitted to the REF and the School’s environment was judged to be conducive to supporting the development of world-leading research.

 

Course structure

You undertake a substantial project in statistics, supervised by an experienced researcher. Some projects are focused on the analysis of particular complex data sets while others are more concerned with generic methodology.

You gain experience of analysing real data problems through practical classes and exercises. The programme includes training in the computer language R.

Modules

The following modules are indicative of those offered on this programme. This list is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.  Most programmes will require you to study a combination of compulsory and optional modules. You may also have the option to take modules from other programmes so that you may customise your programme and explore other subject areas that interest you.

MA885 - Stochastic Processes and Time Series (15 credits)

This module will focus on basic features of stochastic processes and time series analysis. It includes: Markov chains on discrete state spaces, communication classes, transience and recurrence, positive recurrence, stationary distributions. Markov processes on discrete state spaces, exponential distribution, embedded Markov chain, transition graphs, infinitesimal generator, transition probabilities, stationary distributions, skip-free Markov processes. Stationary time series: Stationarity, autocovariance and autocorrelation functions, partial autocorrelation functions, ARMA processes. ARIMA Model Building and Testing: Estimation, Box Jenkins, criteria for choosing between models, diagnostic tests for the residuals of a time series after estimation. Forecasting: Holt-Winters, Box-Jenkins, prediction bounds.

Credits: 15 credits (7.5 ECTS credits).

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MA888 - Stochastic Models in Ecology and Medicine (15 credits)

This module considers the development and application of stochastic models in two specific areas. The ecological part is focused on the analysis of data collected on wild animals. Particular attention will be given to estimating how long wild animals live, and also to estimating the sizes of mobile animal populations. The medical part also considers the estimation of survival, but in this case for human beings, with less data loss due to individuals leaving the study than is typical in ecological studies. In survival data it is often known only that individuals survived for a certain period of time, with exact survival time being unknown. This is called censoring and its implications will be discussed in detail. Outline Syllabus includes: Estimating abundance; estimating survival; using covariates; multi-state models; parameter redundancy; human survival data with censoring; the hazard and related functions; parametric and semiparametric survival models.

Credits: 15 credits (7.5 ECTS credits).

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MA889 - Analysis of Large Data Sets (15 credits)

This module considers statistical analysis when we observe multiple characteristics on an experimental unit. For example, a sample of students' marks on several exams or the genders, ages and blood pressures of a group of patients. We are particularly interested in understanding the relationships between the characteristics and differences between experimental units. Regression methods can be used if one characteristic can be treated as a response variable and the others as explanatory variables. Variable selection on the explanatory variables can be daunting if the number of characteristics is large and suitable methods will be investigated. Outline Syllabus includes: measure of dependence, principal component analysis, factor analysis, canonical correlation analysis, hypothesis testing, discriminant analysis, clustering, scaling, information criterion methods for variable selection, false discovery rate, penalised maximum likelihood.

Credits: 15 credits (7.5 ECTS credits).

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MA890 - Practical Statistics and Computing (15 credits)

Nonparametric Methods: This part of the module comprises approximately 10 lectures on nonparametric methods, showing how they are applied in practice for testing goodness of fit to a distribution, including tests of normality, for testing randomness of a sequence, and for comparing two samples. Practical Statistics: There is no fixed syllabus for this component of the course. Students gain experience of practical data analysis through a series of assessments that confront them with unfamiliar data, which may require the use of techniques introduced in any of the other core modules of the Programme. Statistical Computing: At the start of the module, students are introduced to, and gain experience of, the document preparation system LaTeX, which enables the production of high-quality mathematical documents. Then there are sessions in which students learn the statistical package R, using a mixture of lectures and hands-on computing workshops. The initial aim is for students to gain familiarity with importing and manipulating data, producing graphs and tables, and running standard statistical analyses. The later parts of the module focus on the use of R as a programming language, introducing basic programming mechanisms such as loops, conditional statements and functions. This provides students with the means to develop their own code to undertake non-routine types of analysis if these are not already available in R.

Credits: 15 credits (7.5 ECTS credits).

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MA858 - Computational Statistics (15 credits)

This applied statistics module focusses on problems that occur in the fields of ecology, biology, genetics and psychology. Motivated by real examples, you will learn how to define and fit stochastic models to the data. In more complex situations this will mean using optimisation routines in MATLAB to obtain maximum likelihood estimates for the parameters. You will also learn how construct, fit and evaluate such stochastic models. Outline Syllabus includes: Function optimisation. Basic likelihood tools. Fundamental features of modelling.  Model selection. The EM algorithm. Simulation techniques. Generalised linear models.

Credits: 15 credits (7.5 ECTS credits).

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MA867 - Project (60 credits)

The module, which is compulsory for students of MSc in Statistics and MSc in Statistics with Finance, enables students to undertake an independent piece of work in a particular area of statistics, or statistical finance/financial econometrics and to write a coherent account of the material. A list of possible topics, together with names of Staff willing to supervise these projects, will be circulated to students in the autumn term. A broad range of projectsis available, encompassing both practical data analysis and more methodological work, although projects that are primarily theoretical will typically have obvious practical applications. Students then choose a topic after consultation and agreement with the relevant member of staff. This is done early in the spring term and some preliminary work is done during the spring term, leading to a short presentation at the end of that term. The main part of the project is then undertaken after the examinations in May.

Credits: 60 credits (30 ECTS credits).

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MA881 - Probability and Classical Inference (15 credits)

This module begins by introducing probability, primarily as a tool that underlies the subsequent material on statistical inference. This includes, for example, various notions of convergence for random variables. Classical statistical inference assumes that data follow a probability model with some unknown parameters, and the main aims are to estimate these parameters and to test hypotheses about them. The focus of the module is to develop general methods of statistical inference that can be applied to a wide range of problems. Outline syllabus includes: probability axioms; marginal, joint and conditional distributions; Bayes theorem; important distributions; convergence of random variables; sampling distributions; likelihood; point estimation; interval estimation; likelihood-ratio, Wald and score tests; estimating equations.

Credits: 15 credits (7.5 ECTS credits).

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MA882 - Advanced Regression Modelling (15 credits)

This module covers regression techniques used to understand the effect of explanatory variables on a response, which may be continuous, ordinal or categorical. Issues including general inference, goodness-of-fit, variable selection and diagnostics will be discussed and the material presented in a data-centred way. Outline Syllabus includes: Linear Model: Simple and multiple linear regression including inference (estimation, hypothesis testing and confidence intervals) and diagnostics (detection of outliers, multicollinearity and influential observations). The General linear model, polynomial regression and analysis of variance. Discrete data analysis: Review of Binomial, Poisson, negative binomial and multinomial distributions. Properties, estimation, hypothesis tests. Generalized Linear Model: Estimation, hypothesis testing and model comparison of these models. Diagnostics and goodness-of-fit. Contingency tables: Tests for independence, Measures of association, logistic models, multidimensional tables, log linear models, fitting and model selection.

Credits: 15 credits (7.5 ECTS credits).

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MA883 - Bayesian Statistics (15 credits)

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. Current methods for inference involving posterior distributions typically involve sampling strategies. That is, due to the complicated nature of some posterior distributions, analytic methods fail to provide meaningful summaries. Hence, sampling from the posterior has become popular. A full description of sampling techniques, starting from rejection sampling, will be given. Outline Syllabus includes: Conjugate models (prior and posterior belong to the same family of parametric models). Predictive distributions; Bayes estimates; Sampling density functions; Gibbs and Metropolis-Hastings samplers; Winbugs; Bayesian regression and hierarchical models; Bayesian model choice; Decision theory; Objective priors; Exchangeability.

Credits: 15 credits (7.5 ECTS credits).

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MA884 - Principles of Data Collection (15 credits)

This module will focus on sample surveys, experimental design and clinical trials. It will consider the key principles for designing a survey or an experiment to ensure that any inferences drawn about the population being studied or about the treatments being compared are valid. The discussion of experimental design will be extended into the field of clinical trials (trials conducted on humans) with the added considerations that this introduces. Use will be made of R and Excel for practical examples. Outline Syllabus includes: simple, stratified, cluster and multi-stage sampling; ratio and regression estimators; questionnaire design; completely randomised, randomised block and Latin square designs; factorial designs, fractional replication and confounding; incomplete block designs; analysis of covariance; practical aspects of clinical trials; parallel group trials, sample size; multicentre and crossover trials.

Credits: 15 credits (7.5 ECTS credits).

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Teaching and Assessment

Assessment is through coursework involving: complex theoretical questions, analysis of real-world data using appropriate computing packages over a range of areas of application; written unseen examinations; dissertation.

Programme aims

This programme aims to:

  • To give students the depth of technical appreciation and skills appropriate to masters' level students in Statistics.
  • To equip students with a comprehensive and systematic understanding of theoretical and practical Statistics.
  • To develop students’ capacity for rigorous reasoning and precise expression.
  • To develop students’ capabilities to formulate and solve problems relevant to Statistics.
  • To develop in students appreciation of recent developments in Statistics, and of the links between the theory of Statistics and their practical application.
  • To develop in students a logical, mathematical approach to solving problems.
  • To develop in students an enhanced capacity for independent thought and work.
  • To ensure students are competent in the use of information technology, and are familiar with computers, together with the relevant software.
  • To provide students with opportunities to study advanced topics in Statistics, engage in research at some level, and develop communication and personal skills.
  • To provide successful students with the depth of knowledge of the subject sufficient to enter a career as a professional statistician.
  • To provide successful students with eligibility for exemptions from examinations of the Royal Statistical Society.

Learning outcomes

Knowledge and understanding

You will gain knowledge and understanding of:

  • Systematic understanding of probability and statistics and the range of principles involved.
  • Awareness of links between different statistical concepts and methods.
  • Advanced information technology skills relevant to statisticians.
  • A comprehensive range of methods and techniques appropriate to statistics at the postgraduate level.
  • The role of logical mathematical argument and deductive reasoning.
  • Appreciation of particular subject areas to which statistics is applied, and the importance of the role of statistics in those areas.

Intellectual skills

You develop intellectual skills in:

  • Ability to demonstrate a comprehensive understanding of the main body of statistical knowledge.
  • Ability to demonstrate skill in calculation and manipulation of data.
  • Ability to apply a range of statistical concepts and principles in various challenging contexts.
  • Ability for logical argument.
  • Ability to demonstrate skill in solving complex statistical problems using appropriate and advanced methods.
  • Ability in relevant computer skills and usage.
  • Ability to work with relatively little guidance.
  • Ability to evaluate research work critically.

Subject-specific skills

You gain subject-specific skills in:

  • Ability to demonstrate knowledge of advanced statistical concepts and topics, both explicitly and by applying them to the solution of problems.
  • Ability to demonstrate knowledge of statistical modelling techniques and their application.
  • Ability to abstract the essentials of problems so as to facilitate modelling, statistical analysis and interpretation.
  • Ability to present statistical analyses, including model-fitting, and draw conclusions with clarity and accuracy.

Transferable skills

You will gain the following transferable skills:

  • Problem-solving skills; ability to work independently to solve problems involving qualitative or quantitative information.
  • Communication skills, including the capacity to report to others on analyses undertaken.
  • Computational skills.
  • Information-retrieval skills involving a range of resources.
  • Information technology skills including scientific word-processing.
  • Time-management and organisational skills, as evidenced by the ability to plan and implement efficient and effective modes of working.
  • Skills needed for continuing professional development.

Study support

Postgraduate resources

Kent’s Computing Service central facility runs Windows. Within the School, postgraduate students can use a range of UNIX servers and workstations. Packages available include R, SAS, MATLAB, SPSS and MINITAB.

Dynamic publishing culture

Staff publish regularly and widely in journals, conference proceedings and books. Among others, they have recently contributed to: Annals of Statistics; Biometrics; Biometrika; Journal of Royal Society, Series B; Statistics and Computing. Details of recently published books can be found within our staff research interests.

Global Skills Award

All students registered for a taught Master's programme are eligible to apply for a place on our Global Skills Award Programme. The programme is designed to broaden your understanding of global issues and current affairs as well as to develop personal skills which will enhance your employability.  

Careers

Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers.

Our graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.

Professional recognition

The taught programmes in Statistics and Statistics with Finance provide exemption from the professional examinations of the Royal Statistical Society and qualification for Graduate Statistician status.

Entry requirements

A minimum of 2.2, with a substantial amount of mathematics at university level.

General entry requirements

All applicants are considered on an individual basis and additional qualifications, and professional qualifications and experience will also be taken into account when considering applications. 

International students

Please see our International Student website for entry requirements by country and other relevant information for your country. 

Meet our staff in your country

For more advise about applying to Kent, you can meet our staff at a range of international events.

English language entry requirements

For detailed information see our English language requirements web pages. 

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 through Kent International Pathways.

Research areas

Biometry and ecological statistics

Specific interests are in biometry, cluster analysis, stochastic population processes, analysis of discrete data, analysis of quantal assay data, overdispersion, and we enjoy good links within the University, including the School of Biosciences and the Durrell Institute of Conservation and Ecology. A recent major joint research project involves modelling the behaviour of yeast prions and builds upon previous work in this area. We also work in collaboration with many external institutions.

Bayesian statistics

Current work includes non-parametric Bayes, inference robustness, modelling with non-normal distributions, model uncertainty, variable selection and functional data analysis.

Bioinformatics, statistical genetics and medical statistics

Research covers bioinformatics (eg DNA microarray data), involving collaboration with the School of Biosciences. Other interests include population genetics, clinical trials and survival analysis.

Nonparametric statistics

Research focuses on empirical likelihood, high-dimensional data analysis, nonlinear dynamic analysis, semi-parametric modelling, survival analysis, risk insurance, functional data analysis, spatial data analysis, longitudinal data analysis, feature selection and wavelets.

Staff research interests

Full details of staff research interests can be found on the School's website.

Dr Diana Cole: Senior Lecturer in Statistics

Branching processes in biology; cell division models; ecological statistics; generalised linear mixed models; identifiability.; parameter redundancy.

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Dr Alfred Kume: Senior Lecturer in Statistics

Shape analysis; directional statistics; image analysis.

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Dr Alexa Laurence: Lecturer in Statistics

Medical statistics and applied statistics.

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Dr Fabrizio Leisen: Senior Lecturer in Statistics

Bayesian nonparametrics; MCMC, Urn models; Markov and Levy processes; Move-to-Front and Move-to-Root allocation rules.

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Dr Owen Lyne: Lecturer in Statistics

Stochastic epidemic models; applied probability; simulation; statistical inference; goodness of fit; branching processes; martingales; medical education.

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Dr Rachel McCrea: Research Associate

Integrated population modelling of dependent data structures.

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Professor Byron Morgan: Professor of Applied Statistics

Biometry; cluster analysis; stochastic population processes; psychological applications of statistics; multivariate analysis; simulation; analysis of quantal assay data; medical statistics; ecological statistics; overdispersion; estimation using transforms. 

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Professor Martin S Ridout: Professor of Applied Statistics

Analysis of discrete data in biology; generalised linear models; overdispersion; stochastic models; transform methods.

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Dr Xue Wang: Lecturer in Statistics

Bayesian nonparametric methods; copula function with its applications in finance; wavelet estimation methods.

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Professor Jian Zhang: Professor of Statistics

Semi and non-parametric statistical modelling; statistical genetics with medical applications; Bayesian modelling; mixture models; neuroimaging.

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Enquire or order a prospectus

Resources


Contacts

Admissions enquiries

T: +44 (0)1227 827272

E:information@kent.ac.uk

Subject enquiries

Claire Carter

T: 44 (0)1227 824133

E: smsaspgadmin@kent.ac.uk

School website

Open days

We hold regular Open Events at our Canterbury and Medway campuses. You will be able to talk to specialist academics and admissions staff, find out about our competitive fees, discuss funding opportunities and tour the campuses.

You can also discuss the programmes we run at our specialist centres in Brussels, Athens, Rome and Paris at the Canterbury Open Events. If you can't attend but would like to find out more you can come for an informal visit, contact our information team or find out more on our website.  

Please check which of our locations offers the courses you are interested in before choosing which event to attend.

 

The University of Kent makes every effort to ensure that the information contained in its publicity materials is fair and accurate and to provide educational services as described. However, the courses, services and other matters may be subject to change. Full details of our terms and conditions can be found at: www.kent.ac.uk/termsandconditions.

*Where fees are regulated (such as by the Department of Business Innovation and Skills or Research Council UK) they will be increased up to the allowable level.

School of Mathematics, Statistics and Actuarial Science (SMSAS), Sibson Building, Parkwood Road, Canterbury, CT2 7FS

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Last Updated: 08/07/2014