Image representing Statistical Data Science with an Industrial Placement

Statistical Data Science with an Industrial Placement - MSc

2019

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

2019

Overview

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.

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 consistently high rankings in the last two Research Assessment Exercises.

Statistical Data Science 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.

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.

Your placement

Placements normally commence shortly after completion the taught part of the MSc (June) or after completion of the short dissertation (August) and vary in length from three months to 50 weeks, extending the MSc programme to between 15 and 24 months. The start date and duration depend on the employer. Students on a longer placement module (i.e. 6, 9 or 12 months) can transfer to a shorter module if the placement arrangement changes unavoidably once the student has embarked on it, but if a student cannot complete the minimum of three months placement he/she will be required to transfer to the MSc programme without a placement. Placements may be undertaken in the UK or overseas. 

The University does not guarantee every student will find a placement. Those who do not secure a placement will be transferred to the MSc programme without a placement.

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.

No modules information available for this delivery.

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.

Continuation to indistrial placement

The placement consists of two modules: Industrial Placement Experience and Industrial Placement Report. Four versions of the Experience module exist to cover placements of different lengths. The Experience module is assessed as pass/fail only and the Report module is graded on a categorical scale.

Any student who does not complete both the Industrial Placement Experience module and the Industrial Placement Report module will be transferred to the equivalent non-Year in Industry programme, and the Year in Industry will not be taken into account for the purposes of calculating the award.

Programme aims

  • 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

  • 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

  • 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

  • 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

  • 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.
  • Practical experience of the application in a working environment of knowledge and skills gained through academic study.

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.

Recent 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.

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.  

Entry requirements

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

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

International students

Please see our International Student website for entry requirements by country and other relevant information for your country.  Please note that international fee-paying students cannot undertake a part-time programme due to visa restrictions.

English language entry requirements

The University requires all non-native speakers of English to reach a minimum standard of proficiency in written and spoken English before beginning a postgraduate degree. Certain subjects require a higher level.

For detailed information see our English language requirements web pages. 

Need help with English?

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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Fees

The 2019/20 annual tuition fees for this programme are:

Statistical Data Science with an Industrial Placement - MSc at Canterbury:
UK/EU Overseas
Full-time £7500 £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.* If you are uncertain about your fee status please contact information@kent.ac.uk

General additional costs

Find out more about general additional costs that you may pay when studying at Kent. 

Funding

Search our scholarships finder for possible funding opportunities. You may find it helpful to look at both: