|Duration||Full time 1 year|
Learn the skills needed to collect, analyse, model and extract meaningful information from data in a way that is relevant to a broad range of careers.
Build on your existing mathematical and quantitative skills as you encounter a wide variety of statistical techniques and applications.
Learn how to perform complex statistical analyses and communicate your findings to a variety of audiences.
Taught by experts immersed in cutting-edge research, you will cover both the theory and application of traditional and modern statistical methods from the foundations of statistical theory to the application of cutting-edge regression models.
This degree provides a practical foundation for anyone wishing to pursue a career in statistics and related areas, or students looking to engage in postgraduate research.
This degree is comprised of modules drawing upon Exeter’s considerable expertise in statistics. Over the course of the degree you will learn the skills and techniques required to collect, organise, analyse, interpret and present data. You will become experienced in understanding and modelling structures and patterns in data, as well as when and how to allow for inevitable uncertainties.
The taught component of the programme is completed in June with the project extending over the summer period for submission in September.
Statisticians need a variety of skills covering all aspects of data collection, organisation, analysis, interpretation and presentation. In this module you will learn how to understand and model structures and patterns in data, the principles of uncertainty and how to perform statistical inference. The module will cover both traditional and modern approaches to statistical practice, from the foundations of statistical theory to the application of cutting-edge regression models. Through practical examples, you will gain the skills you need to extract meaningful information from data, how to interpret the results from complex statistical analyses and how to communicate your findings to a variety of audiences.
Bayesian Statistics, Philosophy and Practice
Since the 1980s, computational advances and novel algorithms have seen Bayesian methods explode in popularity, today underpinning modern techniques in data analytics, pattern recognition and machine learning as well as numerous inferential procedures used across science, social science and the humanities. As well as underpinning a philosophical understanding of Bayesian reasoning with theory, we will use software currently used for Bayesian inference in the lab, allowing you to apply techniques discussed in the course to real data.
Applications of Data Science and Statistics
This module will enable you to learn new Data Science and Statistical methods, and to use the techniques learnt in other modules, by working on analyses of real data examples. There will be a strong emphasis throughout on understanding the practical application of statistical and machine learning methods including clustering, data reduction, methods for handling missing data, study design and introductory methods for time series data. Theory and ideas to will be developed to allow the implementation of methods in examples drawn from industry, medicine, finance, public health and environmental challenges, including climate change and air pollution.
Engaging with Research
Critical to every successful academic and industrial career is the ability to keep abreast of developments in your area of expertise. Through engaging with suitable seminars in your specialism, reading research papers and presenting on new developments, you will develop the skills that are essential in a fast-paced and constantly developing environment. You will also be introduced to, and become part of, a thriving research community and will develop key relationships and networking opportunities.
Advanced Topics in Statistics
This module offers an insight to cutting-edge statistical techniques that are at the forefront of current research and application. You will have opportunity to explore a range of topics from time series modelling and forecasting, geostatistics, modelling of extreme values, hierarchical modelling, data fusion, multivariate analysis, computational statistics, and data mining methods, reliability theory and survival analysis, sample survey and experimental design. The choice of topics in any year may change to ensure that the content of the module reflects the rapid change in this exciting area.
Statistical Modelling in Space and Time
Statistical modelling techniques often treat data as independent and identically distributed, but real-world data is often more complex. Data collected in close proximity, in both space and time, are often highly correlated and methods are required that can acknowledge, and exploit, such dependencies within data. In this module, you will learn how to identify and characterise correlations over space and time and gain a thorough understanding of spatial statistics and methods for modelling time series data.
Advanced Statistical Modelling
Statistical modelling lies at the heart of modern data analysis. In this module you will learn how the ideas behind linear regression can be developed into a much broader set of models that can be used when dealing with different types of data and for modelling complex relationships between variables. You will be introduced to the Generalized Linear Model (GLM) and extensions including random effects models, Generalized Linear Mixed Models and Generalized Additive Models. You will gain an understanding of both the theoretical basis of advanced statistical models and their application, with examples of the implementation given through the use real-life examples in practical sessions.
Data Governance and Ethics
Data science, machine learning, artificial intelligence and ‘big data’ have become central to every aspect of modern life. In this module you will examine how these complex and powerful technologies can best be managed and governed for the benefit of society, both now and in the future. You will identify the ethical/legal challenges that are associated with the widespread automation and digitalisation of services, including potential trade-offs between the desire for individual privacy and the institutional commodification of personal data. You will learn how such concerns can be handled and explore the responsibilities of data scientists, and other producers of technologies for data analysis, to ensuring their proper use.
Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition. Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.
Statistical Inference: Theory and Practice
|Terms 2 and 3|
Research Project in Statistics
The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.
Learning and teaching
Statistics at Exeter
The University of Exeter’s is notable for its research in the areas of forecast verification and processing, air quality modelling, spatial epidemiology, environmental hazards, calibration of computer models and quantifying uncertainty in complex systems. Find out more about our research.
Learning and teaching
We believe every student benefits from being taught by experts active in research and practice. You will discuss the very latest ideas, research discoveries and new technologies in seminars and in the field and you will become actively involved in a research project yourself. All our academic staff are active in internationally-recognised scientific research across a wide range of topics. You will also be taught by leading industry practitioners.
Modules are either assessed by coursework only, or a mixture of coursework and an exam. For detailed information on assessment see the module descriptors link from the module codes listed in the ‘structure’ section.
Statisticians are found in many sectors. Demand for graduates with the ability to competently organise and analyse data is increasing as the amount of digital information available rapidly grows.
Your degree will provide you with analytical and statistical skills that are highly sought after by employers. Roles directly relating to a qualification in statistics exist in the public and private sectors and include actuarial analyst, actuary, data analyst or scientist, financial risk analyst, investment analyst, market or operational researcher and statistician.
In addition, this degree will also provide an excellent foundation should you wish to pursue advanced postgraduate research in statistics within academia.
All students, current and alumni, receive support from our dedicated Career Zone team who provide excellent career guidance at all stages of the career planning cycle. The Career Zone provides one-on-one support and is the home of a wealth of business and industry contacts. Additionally, they host useful training events, workshops and lectures which are designed to further support you in developing your enterprise acumen.
As an Exeter graduate you will have access to the Career Zone for life.
Please visit the Career Zone for additional information on their services.
Normally a 2:1 honours degree in a quantitative subject, for example: statistics, mathematics, engineering or physics.
Requirements for international students
If you are an international student, please visit our international equivalency pages to enable you to see if your existing academic qualifications meet our entry requirements.
English language requirements
Overall score 6.5. No less than 6.0 in any section.
Overall score 90 with minimum scores of 21 for writing, 21 for listening, 22 for reading and 23 for speaking.
Pearson Test of English (Academic)
58 with no less than 55 in all communicative skills.
Other accepted tests
Information about other acceptable tests of linguistic ability can be found on our English language requirements page.
Applicants with lower English language test scores may be able to take pre-sessional English at INTO University of Exeter prior to commencing their programme. See our English language requirements page for more information.
Fees and funding
Tuition fees per year 2020/21
- UK/EU: £9,950 full-time;
- International: £20,950 full-time
Fees can normally be paid by two termly instalments and may be paid online. You will also be required to pay a tuition fee deposit to secure your offer of a place, unless you qualify for exemption. For further information about paying fees see our Student Fees pages.
UK government postgraduate loan scheme
Postgraduate loans of up to £10,609 are now available for Masters degrees. Find out more about eligibility and how to apply.
Global Excellence Scholarship
We are delighted to offer Global Excellence Scholarships for students of outstanding academic quality applying to postgraduate Taught programmes starting in autumn 2020.
Please note that this scholarship isn't offered for all our masters programmes.
If you have any questions about any of our taught programmes, please contact us:
Tel: +44 (0) 1392 724061
Web: Enquire online