- 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
Top 20 for Computer Science
20th in The Times and The Sunday Times Good University Guide 2024
Partner to the Alan Turing Institute
Home to Exeter's Institute for Data Science and Artificial Intelligence
Top 20 in the UK for graduate prospects
16th for graduate prospects for Computer Science in the Complete University Guide 2024 (94%)
Entry requirements (typical offer)
|Qualification||Typical offer||Required subjects|
|A-Level||AAA-AAB||GCE AL Maths grade B in Mathematics, Pure Mathematics or Further Mathematics|
|IB||36/666-34/665||HL 6 in Mathematics (Analysis and approaches or Applications and interpretations)|
|BTEC||DDD||Applicants studying a BTEC Extended Diploma will also require GCE AL Maths grade B|
|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|
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
International Foundation programmes
Preparation for entry to Year 1 of an undergraduate degree:
Please note: This programme is currently in development. The modules listed below are indicative of the topic areas you can expect to cover on the course, but are subject to change.
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.
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 core techniques in machine learning and data science. You will also learn how techniques are applied to workflows linked to tackling 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 up to five 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 will give you the opportunity to specialise in areas that are most suited to your interests, giving you a strong foothold for future career development.
Tuition fees for 2024 entry
UK students: £9,250 per year
International students: £27,000 per year
The University of Exeter has many different scholarships available to support your education, including £5 million in scholarships for international students, such as our Global Excellence Scholarships*. Financial support is also 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 apply. See online 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.
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.
Proficiency in a second subject
If you complete 60 credits of modules in one of the subjects below, you may have the words 'with proficiency in [e.g. Social Data Science]' added to your degree title when you graduate.
- A Foreign Language
- Social Data Science
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.
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.
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
- Business Statistician
- Data Analyst
- Data Architect
- Data Scientist
- Machine Learning
- Quantitative Researcher
- Research Analyst
- Research Scientist