| UCAS code | GG16 |
|---|---|
| Duration | 3 years |
| Entry year | 2025 |
| Campus | Streatham Campus |
| Discipline | Data Science |
| Contact | Web: Enquire online |
| Typical offer | |
|---|---|
|
A-Level: ABB-BBB |
Overview
- This course has been developed in collaboration with industry, using current methods, platforms, software and data, to ensure you are fully prepared for the workplace upon graduation
- You will develop fundamental mathematical and computational techniques via a mixture of individual and group learning
- This degree will 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, this course covers the core areas of mathematics and data science while introducing you to applications and social contexts
- Research projects in each academic year will allow you to develop independent research and project management skills in an area of interest, using real world datasets and guided by an academic supervisor
Discover Data Science at the University of Exeter.
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 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
International Foundation programmes
Preparation for entry to Year 1 of an undergraduate degree:
Course content
The BSc Data Science is an innovative interdisciplinary course designed with industry and aimed at those wishing to work or research in the data science sector. The course will cover the core areas of mathematics and computer science. It also includes new modules which will introduce 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, guided by a leading academic supervisor.
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 gain core knowledge and practical skills relating to data structures and algorithms and will practice the techniques and applications of AI and machine learning.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| ECM1400 | Programming | 15 |
| ECM1410 | Object-Oriented Programming | 15 |
| COM1011 | Fundamentals of Machine Learning | 15 |
| ECM1407 | Social and Professional Issues of the Information Age | 15 |
| ECM1413 | Computers and the Internet | 15 |
| ECM1414 | Data Structures and Algorithms | 15 |
| ECM1415 | Discrete Mathematics for Computer Science | 15 |
| ECM1416 | Computational Mathematics | 15 |
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 methods 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.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| ECM2414 | Software Development | 15 |
| ECM2419 | Database Theory and Design | 15 |
| MTH2006 | Statistical Modelling and Inference | 30 |
| COM2011 | Machine Learning and Data Science | 15 |
Optional modules
| Code | Module | Credits |
|---|---|---|
| ECM2434 | Group Software Engineering Project | 15 |
| Select 30 credits from: | ||
| COM2014 | Computational Intelligence | 15 |
| * | Free choice elective | 15 |
Year 3 will comprise group and individual work as you carry out your final year project. 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.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| COM3021 | Data Science at Scale | 15 |
| ECM3401 | Individual Literature Review and Project | 45 |
| COM3023 | Machine Learning and AI | 15 |
Optional modules
| Code | Module | Credits |
|---|---|---|
| Select up to 45 credits: | ||
| ECM3408 | Enterprise Computing | 15 |
| ECM3412 | Nature Inspired Computation | 15 |
| ECM3422 | Computability and Complexity | 15 |
| ECM3423 | Computer Graphics | 15 |
| ECM3428 | Algorithms that Changed the World | 15 |
| ECM3446 | High Performance Computing | 15 |
| MTH3019 | Mathematics: History and Culture | 15 |
| MTH3024 | Stochastic Processes | 15 |
| MTH3028 | Statistical Inference: Theory and Practice | 15 |
| MTH3041 | Bayesian statistics, Philosophy and Practice | 15 |
| MTH3044 | Bayesian Data Modelling | 15 |
| You may select up to 30 credits of other options: | ||
| EMP3001 | Commercial and Industrial Experience | 15 |
| * | Free choice elective - up to 30 credits | 30 |
I chose Data Science because I didn’t want to just focus on coding. Exeter is one of the only universities with a course that lets me combine my interests and passion in all areas, such as statistics and machine learning.
Already during my first two months I’ve touched upon mathematics and the basics of programming. It’s really exciting and will open doors to a huge range of careers.
Natalie
Studying BSc Mathematics and Data Science at the University of Exeter
Fees
Tuition fees for 2024 entry
UK students: £9,250 per year
International students: £27,000 per year
Scholarships
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.
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.
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
- Law
- Social Data Science
- Entrepreneurship
- Leadership
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 three-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
