MSci Data Science
| UCAS code | GG17 |
|---|---|
| Duration | 4 years |
| Entry year | 2026 |
| Campus | Streatham Campus |
| Typical offer | |
|---|---|
|
A-Level: ABB-BBB |
Why study MSci Data Science at Exeter?
- This course has been developed in collaboration with industry, using current methods, platforms, software and data, to ensure you are fully prepared for workplace practice upon graduation
- Throughout your studies at Exeter, you will develop fundamental mathematical and computational techniques via a mixture of individual and group learning
- Our programmes support you in becoming an outstanding, dynamic problem solver with an excellent technical skillset, preparing you for a fantastic array of professions that require the technical expertise of a data scientist
- Taught by active researchers who are experts in their fields, covering the core areas of mathematics and computer science while introducing you to applied data science as well as social context
- Research projects in each academic year will allow you to develop research and project management skills in an area of interest, using real world datasets and guided by a leading academic supervisor
- Pursue your studies to Masters level in your final year with the freedom to choose advanced modules to suit your interests
Discover Data Science at the University of Exeter.
Top 20 in the UK for Computer Science
18th in the Complete University Guide 2026
Partner to the Alan Turing Institute
Home to Exeter's Institute for Data Science and Artificial Intelligence
Top 15 in the UK for graduate prospects
Joint 12th for graduate prospects for Computer Science in the Complete University Guide 2026 (94%)
Entry requirements (typical offer)
| Qualification | Typical offer | Required subjects |
|---|---|---|
| A-Level | AAA-AAB | GCE A-Level Maths grade B in Mathematics, Pure Mathematics or Further Mathematics |
| IB | 36/666-34/665 | HL 5 in Mathematics (Analysis and approaches or Applications and interpretations) |
| BTEC | DDD | Applicants studying a BTEC Extended Diploma are also required to achieve a grade B at A-Level in Mathematics |
| GCSE | 4/C | Grade 4/C in GCSE English Language |
| Access to HE | 30 L3 credits at Distinction Grade and 15 L3 credits at Merit Grade | 12 L3 credits at Merit Grade in an acceptable Mathematics subject area |
| T-Level | T-Levels not accepted | N/A |
| Contextual Offer | A-Level: ABB-BBB |
Specific subject requirements must still be achieved where stated above. Find out more about contextual offers. |
| Other accepted qualifications | ||
| English language requirements |
International students need to show they have the required level of English language to study this course. The required 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. |
|
NB General Studies is not included in any offer.
Grades advertised on each programme webpage are the typical level at which our offers are made and provide information on any specific subjects an applicant will need to have studied in order to be considered for a place on the programme. However, if we receive a large number of applications for the programme we may not be able to make an offer to all those who are predicted to achieve/have achieved grades which are in line with our typical offer. For more information on how applications are assessed and when decisions are released, please see: After you apply
Course content
MSci Data Science is an innovative interdisciplinary course designed with industry and aimed at those wishing to work or research in the data science sector.
In your first year, you will be introduced to the fundamental technical and professional skills needed to successfully engage with machine learning, artificial intelligence and data science.
You will learn core knowledge and practical skills relating to data structures and algorithms that are commonly applied in this topic area, as well as some of the most common techniques and applications of AI and machine learning.
In year 2 you will gain theoretical and practical understanding of some of the more advanced techniques in machine learning and data science. You will also learn how data science is linked to challenges in real-world social issues.
Through lectures and practical exercises, you will develop vital professional and interpersonal skills needed to work effectively in the mathematical and digital sector, including project management and teamwork.
In your third year, alongside your individual project, you will explore the world of “big data” and its demands for high-performance computing (HPC) to take advantage of modern learning and statistical methods applied to novel datasets. You will then select optional modules, ensuring you gain a broad range of knowledge across Data Science.
Your final year will comprise group and individual work as you carry out your final year project. Advanced optional modules allow you to specialise in areas that are most suited to your interests, giving you a strong foothold for future career development.
You may notice changes to some of our modules over the coming months. This is because we are making space for the following:
- Minors: Future Skills Pathways - Alongside your main degree you may be eligible (depending on your course) to choose modules from another subject to broaden your skills and interests.
- Skills to Thrive built into every degree - Essential skills for your future, including communication, problem-solving, teamwork and digital confidence.
- Increased innovation and wellbeing - More room for creative learning, real-world projects and a healthier study rhythm.
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.
120 credits of compulsory modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Fundamentals of Machine Learning | 15 | |
| Programming | 15 | |
| Social and Professional Issues of the Information Age | 15 | |
| Object-Oriented Programming | 15 | |
| Computers and the Internet | 15 | |
| Data Structures and Algorithms | 15 | |
| Discrete Mathematics for Computer Science | 15 | |
| Computational Mathematics | 15 | |
Please note that the module information displayed here is subject to change.
105 credits of compulsory modules, 15 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Machine Learning and Data Science | 15 | |
| Team Project | 15 | |
| Software Development | 15 | |
| Database Theory and Design | 15 | |
| Statistical Modelling and Inference | 30 | |
| Data Science in Society | 15 | |
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Computational Intelligence | 15 | |
| Artificial Intelligence and Applications | 15 | |
| Outside the box: Computer Science Research and Applications | 15 | |
Please note that the module information displayed here is subject to change.
75 credits of compulsory modules, 45 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Data Science at Scale | 15 | |
| Probabilistic Machine Learning | 15 | |
| Individual Literature Review and Project | 45 | |
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Computer Vision | 15 | |
| Social Networks and Text Analysis | 15 | |
| Enterprise Computing | 15 | |
| Nature-Inspired Computation | 15 | |
| Computability and Complexity | 15 | |
| Algorithms that Changed the World | 15 | |
| High-Performance Computing | 15 | |
| Commercial and Industrial Experience | 15 | |
| Mathematics: History and Culture | 15 | |
| Stochastic Processes | 15 | |
| Statistical Inference | 15 | |
| Bayesian Statistics, Philosophy and Practice | 15 | |
Please note that the module information displayed here is subject to change.
60 credits of compulsory modules, 60 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Group Development Project | 30 | |
| Individual Research Project | 30 | |
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Network Science | 15 | |
| Text Mining and Natural Language Processing | 15 | |
| Deep Learning | 15 | |
| Generative AI Applications | 15 | |
| Large Language Models and Applications | 15 | |
| Machine Learning | 15 | |
| Evolutionary Computation and Optimisation | 15 | |
| Computer Modelling and Simulation | 15 | |
| Computer Vision | 15 | |
| Social Networks and Text Analysis | 15 | |
| Building Secure and Trustworthy Systems | 15 | |
| Security Assessment and Validation | 15 | |
| Statistical Modelling in Space and Time | 15 | |
| Bayesian Statistics, Philosophy and Practice | 15 | |
| Statistical Data Modelling | 15 | |
Fees
Tuition fees for 2026 entry
UK students: £9,790 per year
International students: £31,200 per year
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 scholarships for sport, music and other achievements, alongside regional and partner awards such as Chevening, The Beacon Trust and the British Council. Financial support is available for students from disadvantaged backgrounds, lower income households and other under-represented groups to help them access, succeed and progress through higher education.
* Terms and conditions, including deadlines, apply. See our website for details.
Learning and teaching
Lectures, seminars and workshops
We make use of a variety of teaching styles, including lectures, seminars, workshops and tutorials. Most modules involve two or three lectures per week, so you would typically have about 10 lectures each week. In addition, workshops and tutorials support and develop what you’ve learnt in lectures and enable you to discuss the lecture material and coursework in more detail. You’ll have over 15 hours of direct contact time per week with your tutors and you will be expected to supplement your lectures with independent study. You should expect your total workload to average about 40 hours per week during term time.
Virtual learning environment
We’re actively engaged in introducing new methods of learning and teaching, including increasing use of interactive computer-based approaches to learning through our virtual learning environment, where the details of all modules are stored in an easily navigable website. You can access detailed information about modules and learning outcomes and interact through activities such as the discussion forums.
A research and practice led culture
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.
Assessment
Modules are assessed by a combination of continuous assessment through small practical exercises, project work, essay writing, presentations and exam.
Optional modules outside of this course
Each year, if you have optional modules available, you can take up to 30 credits in a subject outside of your course. This can increase your employability and widen your intellectual horizons.
Minors: Future Skills Pathways
You can study a Future Skills Pathway alongside your main degree by choosing up to 30 credits of modules from a different subject area in your second and final years.
World-class facilities
Our latest computing facilities are world-class spacious teaching labs allowing comfortable, collaborative working in a sensory-friendly environment.
Your future
There is an established strong market demand for suitably skilled data scientists and data science skills are increasingly being sought across the sectors, particularly by the finance and accounting industries, supermarkets, online retailers such as Amazon, and the NHS.
This Data Science course has been developed with partner employers, including IBM, the Met Office, South West Water, Black Swan and Oxygen House and has been designed to deliver skills that are most valued by employers. Modules will use the employers’ methods, platforms, software and data, to ensure that they are fully reflective of workplace practice. Throughout your studies you will conduct individual and group projects using real world data sets.
This course will prepare you to be an outstanding dynamic problem solver with an excellent technical skillset. In addition to learning the core principles of Mathematics and Computer Science, you will learn soft skills that employers have told us they are looking for, such as communication and presentation skills, and the ability to work effectively in a team.
The inclusion of individual- and group-based project work in every academic year will offer you an opportunity to apply your skills to solve real world problems and prepare you for future employment.
Industrial Experience
As part of the four-year degree, you can choose to take an optional Commercial and Industrial Experience module during the vacation before the third year (subject to availability). This very rewarding opportunity allows you to gain paid work experience while earning credits towards your degree programme. Following the placement you can report on your experience which, alongside a report from the employer, enables you to count your experience as a third-year optional module. We have excellent links with employers and can provide assistance in finding suitable employment.
Career Paths
The broad-based skills acquired during your degree will give you an excellent grounding for a wide variety of careers, not only those related to Data Science but also in wider fields. Examples of roles recent graduates are now working as include:
- Analytics Manager
- Business Intelligence
- Analyst
- Business Statistician
- Data Analyst
- Data Architect
- Data Scientist
- Machine Learning
- Engineer
- Quantitative Researcher
- Research Analyst
- Research Scientist
