MSc Applied Data Science and Statistics
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | 1234 |
|---|---|
| Duration | 1 year full time 2 years part time |
| Entry year | 2026 |
| Campus | Streatham Campus |
| Typical offer | A good degree (normally a 2:2). |
|---|---|
| UCAS code | |
|---|---|
| Duration | 2 years full time |
| Entry year | 2026 |
| Campus | Streatham Campus |
| Typical offer | A good degree (normally a 2:2). |
|---|---|
Why study MSc Applied Data Science and Statistics at Exeter?
- This well-established conversion course, with a network of over 600 graduates, will enable you to learn data science skills alongside the fundamental mathematics that underpins all data.
- You will learn to programme in Python and R (no prior experience of programming is required) and explore a wide variety of applications to prepare you for a career working with data in a variety of sectors.
- Benefitting from the skills and experience of our academics in the Mathematics and Statistics department, you will work extensively with data and gain the ability to perform statistical analysis to answer questions, and understand how to interpret and communicate results in the presence of bias and uncertainty.
- You will graduate with the capability to extract otherwise-hidden information within data and use it to make informed ethical decisions, with the skills needed to become a rigorous and responsible data scientist or analyst.
Fast Track (current Exeter students)
Applied Data Science and Statistics MSc at the University of Exeter.
Top 20 in the UK for Mathematics and Computer Science
20th for Mathematics in The Times and The Sunday Times Good University Guide 2026; 17th for Computer Science in the Complete University Guide 2027
Long-established partnership with the Alan Turing Institute and home to the Institute of Data Science and Artificial Intelligence
Excellent facilities spanning a wide range of machine types and software ecosystems alongside world-class computer science labs
Entry requirements
A good degree (normally a 2:2).
Successful applicants will usually have at least an A-level or equivalent in Mathematics and/or have received quantitative skills training as part of their undergraduate programme or professional experience.
Prior experience of coding is not necessary on this course.
Please also see our guidance on essential documentation required for an initial decision on taught programme applications.
Entry requirements for international students
English language requirements
International students need to show they have the required level of English language to study this course.
The required IELTS test scores for this course fall under Profile B1.
Please visit our English language requirements page to view the required test scores and equivalencies from your country.
"The course was an extensive coverage of data science and stats, with lots theoretical and practical learning. All the lecturers I had were fantastic. They were always very willing to help, super approachable and really friendly. It was so nice to be able to feel like I had a proper relationship with them.
My dissertation supervisors were the best supervisors I could’ve asked for. They’d help whenever and always provided brilliant guidance allowing me to do my best.
Now I’ve graduated I’m working at Mazars as a Data Analyst. It’s been really nice to use the things I learnt during my masters in my career, and I’ve felt unbelievably well prepared going into my professional life."
Matt
MSc Applied Data Science and Statistics graduate
Course content
The programme will include applications across a wide variety of sectors and help you develop innovative and responsible approaches to the use of data. You will cover the entire spectrum from collection through to interrogation and analysis, interpretation, visualisation, and communication.
Our data governance and ethics module is essential to this programme – you will learn from colleagues in our faculty of Humanities, Arts and Social Sciences how the complex technologies behind data science can be managed and governed for the benefit of society now and in the future, as well as exploring your responsibility as a data scientist for ensuring that data and the technologies used to harvest and analyse it are used ethically.
Assessments will be based on a combination of exams, group and individual project work, practical data analysis, visualisation and communication skills.
You do not need any prior experience of coding to study this programme. Our Introduction to Data Science and Statistical Modelling (MTHM502) and Applications of Data Science and Statistics (MTHM503) give you a great introduction to foundational mathematics and probability as well as programming in R and Python.
The course is based around open source software (Python and R) meaning that you will be well-equipped to apply your new skills to any setting when you graduate. As a student you will also have access to other programmes, such as Matlab, through the University.
All aspects of the course are designed with windows, mac and linux in mind.
The taught component of the programme is completed in June and you will complete your Applied Data Science and Statistics project (dissertation) over the summer period for submission in September. You can submit your own project idea or take up an idea proposed by an external organisation, supervisors from the Applied Data Science and Statistics teaching team or from colleagues across the University such as from our Centre for Computational Social Science (C2S2) The dissertation is an extensive project of approximately 15,000 words that involves project planning, analytical, experimental or empirical results and their interpretation, showing how the goals of the project have been met.
The modules below 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.
Please note that the module information displayed here is subject to change.
180 credits of compulsory modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Advanced Topics in Statistics | 15 | |
| Quantitative Methods and AI for Environmental Challenges | 15 | |
| Introduction to Data Science and Statistical Modelling | 15 | |
| Applications of Data Science and Statistics | 15 | |
| Applied Data Science and Statistics Project | 60 | |
| Data Science and Statistical Modelling in Space and Time | 15 | |
| Statistical Data Modelling | 15 | |
| Communicating Data Science | 15 | |
| Data Governance and Ethics | 15 | |
MTHM017: Advanced Topics in Statistics
This module offers an insight to cutting-edge statistical learning techniques that are at the forefront of current research and application. You will have opportunity to explore a range of important and current topics in statistics and data science, for example Bayesian computation, causality, agentic coding, statistics in the media and simulation. 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. The aims are to expose the student to some recent developments in statistics and data science; to give the student exposure to cutting edge topics and current research trends.
MTHM065: Quantitative Methods and AI for Environmental Challenges
This module provides a comprehensive introduction to quantitative approaches for understanding and addressing environmental challenges. You will learn to apply statistical analysis, spatial modelling, and other advanced quantitative techniques to environmental datasets. Using real-world case studies, you will explore issues such as climate change, air pollution, biodiversity loss, and extreme weather events.
The module emphasises developing practical skills to analyse and interpret data, draw meaningful conclusions, and support evidence-based decision-making.
Content is regularly updated to reflect the latest research and methodological advances, ensuring you gain cutting-edge skills relevant to contemporary environmental problems.
MTHM502: Introduction to Data Science and Statistical Modelling
In this module you will be equipped with the tools required to collate, import and manipulate data together with methods for basic inference including probability, sampling variability, confidence intervals. You will be introduced to different types and sources of data and the tools for performing initial data analysis including producing simple graphical summaries of data and more sophisticated methods for visualising structures in data. You will learn the essential mathematical techniques that are required for the implementation and interpretation of statistical and machine learning methods.
MTHM503: 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 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.
Pre-requisites: None
MTHM504: Applied Data Science and Statistics Project
In this module, you will work on a research problem in the application of Data Science and Statistics. You will apply your understanding of the underlying concepts of Data Science and Statistics together with the methods and tools that you have learned to a problem in an applied field. The project will require understanding of the setting, a critical review of possible approaches, choice of appropriate methodology, an extended piece of data analysis and a clear and concise write up of the background, data, methodology, results and conclusions. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation, describing your research and its results. Research topics can be selected from across the breadth of the application of Data Science and Statistics.
This module aims to give you in-depth experience of applying Data Science and Statistics to real-world problems, preparing you for work in a commercial setting or further post-graduate work. The module aims to build on the knowledge and skills you have acquired in the taught modules of the programme to allow you to investigate an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis, interpretation and presentation of results.
MTHM505: Data Science and Statistical Modelling in Space and Time
In this module, you will learn how to model data that exhibit correlations over space or time. You will explore both the theoretical foundations and practical implementation of statistical methods for correlated data, including Gaussian processes for spatial data and ARIMA/state-space models for time series.
Through practical exercises and coursework, you will gain hands-on experience applying these methods to real-world datasets, including examples from environmental science and computer modelling. By the end of the module, you will be able to model and analyse correlated data effectively and communicate your results clearly using appropriate software tools.
MTHM506: Statistical Data Modelling
Statistical modelling lies at the heart of modern data analysis and is a vital part of the wider landscape of data science/machine learning/AI. There is currently an increasing pressure to regulate AI (used as a general umbrella term), so the future lies in interpretable and explainable AI approaches. As it happens, statistical modelling is both interpretable and explainable and has been used for the last 50 years or so. The point of this module is to introduce statistical modelling as such an approach and illustrate how it can be used to conduct both advanced and flexible data analyses with outputs that can be directly used for decision making. The module starts from simple linear regression familiar from most foundation courses in statistics and places this is the very broad framework of statistical data modelling. Generalized Additive Models (non-linear, hierarchical regression) will be introduced as a unifying modelling framework, that includes estimation, validation, selection and uncertainty quantification as part of the framework. The module will provide you with a toolbox and the ability to analyse any real world data set, including binary data, count data, contingency tables, data with temporal and spatial structure as well as data that are missing or partially missing. We will use the statistical software R (Rstudio) as the main platform to fit this wide range of models, and will use it in practical sessions so that, as well as a sound theoretical basis, you will develop an understanding of how to apply techniques discussed in the module in practical data analysis. The module will introduce a plethora of real data sets spanning a wide range of applications such as public health, weather, climate, ecology, biology, epidemiology, natural hazards and many others.
MTHM507: Communicating Data Science
Critical to every successful academic and industrial career is the ability to communicate data analysis in your area of expertise. Through engaging with and discussing papers and data in this data science specialism we will explore the techniques of reading research/policy papers and presenting on new data analysis developments. You will develop the skills that are essential in a fast-paced and constantly developing environment.
This module aims to introduce you to the important aspects of how to develop your own original consulting/research analyses and how to communicate them effectively. To develop in you the key skills required to stay up-to-date with and communicate your subject knowledge and succeed in the professional and academic environment.
SOCM033: Data Governance and Ethics
Data science, machine learning, artificial intelligence and 'big data' have become central to every aspect of social life. How can these complex and powerful technologies best be managed and governed for the benefit of society now and in the future? In this module you will: (1) identify some of the main risks and ethical/legal challenges involved in the widespread automation and digitalisation of services characterising 21st century life (for example, the clash between individual desire for privacy, frameworks for data ownership and the institutional commodification of personal data); (2) examine whether and how such concerns can be handled; and (3) discuss the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.
Please note that the module information displayed here is subject to change.
If you have chosen to study Applied Data Science and Statistics with Professional Placement, your course will be structured as follows:
Year 1 – study all the 15 credit taught modules in the module table above and secure a work placement for year two.
Year 2 – take part in a work placement and complete your Applied Data Science project (dissertation).
During your work placement you will gain extensive practical work experience, giving you the chance to apply the knowledge and skills you have acquired from taught modules to authentic problem solving in a professional environment. Collaborating with your work placement employer, you will identify a project that will constitute your MSc dissertation. Ideally, the dissertation should be based on the work that you will undertake during the placement, but this is not compulsory. The dissertation is an extensive project of approximately 15,000 words that involves project planning, analytical, experimental or empirical results and their interpretation, showing how the goals of the project have been met.
120 credits of compulsory modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| MSc Professional Placement | 60 | |
| Applied Data Science and Statistics Project | 60 | |
MTHM053: MSc Professional Placement
This module will provide you with extensive practical work experience in a work environment that is of direct relevance to your development as an experienced professional.
The professional placement will give you the opportunity to apply the knowledge and skills that you have obtained from taught modules to real situations at a professional level. You will be expected to use your own initiative and creativity to integrate the knowledge that you have gained in several areas of your degree programme to deliver appropriate technical solutions and analysis in a professional manner.
Course variants
Applied Data Science and Statistics with Professional Placement MSc
- Combine your masters in Applied Data Science and Statistics with work experience in the UK, putting your learning into practice while studying
- Studied over two years, you’ll have the opportunity to gain valuable professional experience by completing a 9-12month work placement in a role relevant to your degree
- Become a sought-after professional with strong industry experience
Fees
2026/27 entry
UK fees per year:
£14,300 full-time
With Professional placement (second year) £2,860 full-time
International fees per year:
£30,300 full-time
With Professional placement (second year) £6,060 full-time
Scholarships
The University of Exeter offers a wide range of scholarships to support your education, with £7 million available for international students applying to study with us in the 2026/27 academic year, including our prestigious Exeter Excellence Scholarships. We also provide awards for sport, music and other achievements, as well as regional and partner scholarships with organisations such as Chevening, The Beacon Trust and the British Council. For more information on scholarships and other financial support, please visit our scholarships and bursaries page.
University of Exeter Alumni Scholarship
We are pleased to offer the University of Exeter Alumni Scholarship, a scholarship for University of Exeter alumni beginning a standalone postgraduate programme in 2026/27 with us a scholarship worth 20% of the cost of your first year tuition fees.
Terms and conditions, including deadlines, apply.
Teaching and research
Teaching on this programme is delivered through a mix of lectures, projects, group work and hands-on lab sessions. Many of your lectures will be interactive combining a blend of classroom learning, coding and data analysis. Group and individual projects will be undertaken using real data and will often focus on topical challenges which are the focus of current research.
Assessments
The assessment strategy for each module is explicitly stated in the full module descriptions given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods include essays, closed book tests, exercises in problem solving, use of the Web for tool-based analysis and investigation, mini-projects, extended essays on specialised topics, and individual and group presentations.
Artificial Intelligence at Exeter
The University has invested £50 million in the development of its Data Science and Artificial Intelligence capabilities. The Accelerating Data Science and Artificial Intelligence (ADA) project has been running since 2023 and has invested in teaching, research and infrastructure which this programme benefits from.
Teaching on this degree is directly influenced by research undertaken within Exeter’s Institute for Data Science and Artificial Intelligence which provides a hub for data-intensive science and artificial intelligence activity within the University.
The institute’s vision for data science is to find new means of interrogating and understanding data and to apply cutting-edge data analytical methodologies to diverse questions. It is a truly interdisciplinary research community made up of data scientists, mathematicians, and computer specialists across many of Exeter’s research groups and departments.
Internationally recognised research
We believe every student benefits from being taught by experts active in research and practice. All our academic staff are active in internationally-recognised scientific research across a wide range of topics. You will discuss the very latest ideas, research discoveries and new technologies, becoming actively involved in a research project yourself.
Academic partners
Our long-established partnership with the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence, means that we have strong connections to the UK AI research community. Currently we host 11 Turing Fellows and one Turing AI Fellow at the University. Turing Fellows are established scholars with proven research excellence in data science, AI, or a related field. Lectures, conferences and seminars organised by Turing and the Turing University Network are usually open to our students to attend either in person or online.
Supportive environment
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. We operate an open door policy, so it is easy to consult individual members of staff or to fix appointments with them via email. As a friendly group of staff, you will get to know us well during your time here.
Careers
Employer demand for statistically-trained data scientists is high.
A World Economic Forum report ‘The Future of Jobs 2023’ projects that the demand for data analysts and scientists, big data specialists, business intelligence analysts, database and network professionals and data engineers is growing by 30 to 35%.
Graduate destinations
Whether you’re looking to take your career in a new direction or for an MSc that will sit alongside your undergraduate degree to land you an exhilarating graduate job, you’re unlikely to find a better choice than Applied Data Science and Statistics. Previous graduates from this programme have gone on to work for a variety of organisations from small start ups through to large multinational corporations in locations across the world.
Dedicated careers support
You will receive support from our dedicated Career Zone team, who provide excellent career guidance at all stages of career planning. The Career Zone provides one-on-one support and is home to 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. Please visit the Career Zone for additional information on their services.
