Statistical Data Science - MSc
Postgraduate Open Event
Join us at our Postgraduate Open Event on 17 November to find out why you belong at Kent. You can choose to join us in person, or book to attend virtually.
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.
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.
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.
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.
Entry requirements
You are more than your grades
For 2022, in response to the challenges caused by Covid-19 we will consider applicants either holding or projected a 2:2. This response is part of our flexible approach to admissions whereby we consider each student and their personal circumstances. If you have any questions, please get in touch.
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 relevant experience may 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. Due to visa restrictions, students who require a student visa to study cannot study part-time unless undertaking a distance or blended-learning programme with no on-campus provision.
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.
Form
Course structure
Duration: 1 year full-time
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.
Compulsory modules currently include
MAST8670 - Project (60 credits)
The module 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.
There is no specific syllabus for this module.
MAST8820 - Statistical Data Modelling (15 credits)
Linear model. Least squares. General linear model; simple and multiple regression, polynomial regression. Model selection, residuals, outliers, diagnostics. Analysis of variance. Generalised linear model.
Discrete data analysis. Review of Binomial, Poisson, negative binomial and multinomial distributions. Properties, estimation, hypothesis tests.
Contingency tables. Tests for independence. Measures of association. Logistic models.
Multidimensional tables. Log–linear models; fitting and model selection.
MAST9420 - Data Science with R (15 credits)
Introduction: Machine learning and data visualisation with R.
Classification and prediction: Generalised linear model (GLM), linear discrimination analysis (LDA), k-nearest neighbors (KNN). R-based worked examples.
Resampling methods: Cross-validation (CV) and bootstrap. R-based worked examples.
Regression tree-based methods: Classification and regression trees (CART), bagging, random forests and boosting. R-based worked examples.
Support vector machines (SVM): Support vector classifier, regression SVM. R-based worked examples.
Machine Learning in Action:
(a) Biomedical and health data analysis;
(b) Bond default data analysis;
(c) Insurance data analysis;
(d) Financial data analysis;
(e) Other big data analysis.
MAST7011 - Bayesian Statistics with Stan and Python (15 credits)
Bayes Theorem for density functions; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs and Metropolis-Hastings samplers; Winbugs/OpenBUGS; Bayesian hierarchical models; Bayesian model choice; Objective priors; Exchangeability; Choice of priors; Applications of hierarchical models.
MAST7012 - "Statistical Consultancy and Data Presentation" (15 credits)
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, 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)
In addition, for level 7 students:
• Advanced presentation of data, such as visualisation of data points on a map
• Further extensions to Linear and Generalised Linear Models, such as hierarchical generalised linear models
MAST7053 - "Statistical Learning for Data Scientists" (15 credits)
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.
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.
In addition, for level 7 students: advanced EM algorithm methods, advanced simulation methods, writing R programs for advanced methods and applications.
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.
Fees
The 2022/23 UK fees for research courses have not yet been set by the UK Government.
- Home full-time £9300
- EU full-time £13000
- International full-time £17400
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.* If you are uncertain about your fee status please contact information@kent.ac.uk.
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.
Additional costs
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:
- University and external funds
- Scholarships specific to the academic school delivering this programme.
Scholarships
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.
Search scholarshipsWhich one's right for you?
Some other similar courses you may be interested in.
Independent rankings
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.
Research
Research areas
Statistical Ecology
There has been research in the area of statistical ecology at Kent for many years. We are part of the National Centre for Statistical Ecology (NCSE), which was established in 2005. For details of the work of the NCSE, see www.ncse.org.uk
Bayesian statistics
The research conducted in this area at Kent is mainly on Bayesian variable selection, Bayesian model fitting, Bayesian nonparametric methods, Markov chain Monte Carlo with applications.
Biological and health statistics
Research is focused on statistical modelling and inference in biology and genetics with applications in complex disease studies. Over the past few decades, large amounts of complex data have been produced by high through-put biotechnologies. The grand challenges offered to statisticians include developing scalable statistical methods for extracting useful information from the data, modelling biological systems with the data, and fostering innovation in global health research.
Machine learning
This theme encompasses both theory and applications. Theory is involved with supervised and unsupervised learning, matrix factorisation, modelling of high-dimensional time series, differential privacy, deep learning and networks, shape analysis and statistics on manifolds, and neuroimaging. Applications in biology, industry, medicine and psychiatry. Often new computational methods are the key to analysing complex big data problems.
Nonparametric statistics
In order to describe the data, it is common in statistics to assume a specific probability model. Unfortunately, in many practical applications (for instance in economics, population genetics and social networks) it is not possible to identify a specific structure for the data. Nonparametric methods provide statistical tools for addressing inference in these situations.
Economics and finance
At Kent there is particular interest in the use of nonparametric methods including quantile regression and Bayesian nonparametric approaches. Application areas include modelling of the business cycle and capacity utilisation, calculating sovereign credit ratings, modelling of stock return data, and predicting inflation.
Staff research interests
Full details of staff research interests can be found on the School's website.
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.
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.
Apply now
Learn more about the applications process or begin your application by clicking on a link below.
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Contact us
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MSc at Canterbury
International student enquiries
T: +44 (0)1227 823254
E: internationalstudent@kent.ac.uk