The International Master’s in Statistical Data Science develops your practical, statistical and computing skills to prepare you for a professional career in statistics or as a solid basis for further research in the area.
A good first degree (or the equivalent) in an appropriate quantitative subject.
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.
Please see our International website for entry requirements by country and other relevant information. Due to visa restrictions, international fee-paying students cannot study part-time unless undertaking a distance or blended-learning programme with no on-campus provision.
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.
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.
Duration: 2 years full-time
Linear algebra, analysis, regression and probability and inference are core topics for the first year of this two-year programme, which also includes a dissertation module. In the second year, stochastic models and processes, Bayesian statistics and the analysis of large data sets are among the range of topics explored.
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.
The programme is assessed by coursework involving: complex theoretical questions, analysis of real-world data using appropriate computing packagse over a range of areas of application; written unseen examinations. In the second year, there is also a substantial dissertation.
This programme aims to:
You will gain knowledge and understanding of:
You develop intellectual skills in:
You gain subject-specific skills in:
You will gain the following transferable skills:
The 2020/21 annual tuition fees for this programme are:
For details of when and how to pay fees and charges, please see our Student Finance Guide.
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 email@example.com
Find out more about general additional costs that you may pay when studying at Kent.
Search our scholarships finder for possible funding opportunities. You may find it helpful to look at both:
In The Complete University Guide 2020, the University of Kent was ranked in the top 10 for research intensity. This is a measure of the proportion of staff involved in high-quality research in the university.
Please see the University League Tables 2020 for more information.
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.
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.
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.
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.
Full details of staff research interests can be found on the School's website.
Branching processes in biology; cell division models; ecological statistics; generalised linear mixed models; identifiability.; parameter redundancy.View Profile
Shape analysis; directional statistics; image analysis.View Profile
Medical statistics and applied statistics.View Profile
Bayesian nonparametrics; MCMC, Urn models; Markov and Levy processes; Move-to-Front and Move-to-Root allocation rules.View Profile
Stochastic epidemic models; applied probability; simulation; statistical inference; goodness of fit; branching processes; martingales; medical education.View Profile
Integrated population modelling of dependent data structures.View Profile
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.View Profile
Bayesian nonparametric methods; copula function with its applications in finance; wavelet estimation methods.View Profile
Semi and non-parametric statistical modelling; statistical genetics with medical applications; Bayesian modelling; mixture models; neuroimaging.View Profile
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.
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.
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.
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.
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.
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