BSc Data Science (Defence)
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | GG23 |
|---|---|
| Duration | 3 years |
| Entry year | 2027 |
| Campus | Streatham Campus |
| Typical offer | A levels: AAA - AAB |
|---|---|
|
A-Level: ABB-BBB |
Why study BSc Data Science (Defence) at Exeter?
- Learn about Data Science and its applications in the UK defence sector, exploring how different organisations (governmental and commercial) collaborate and leverage computing technology to support national defence and security.
- Gain practical experience through defence-relevant teaching, industry projects, hackathons, guest lectures, placements and employer-led activity.
- Benefit from £6.4 million investment in specialist AR, VR, XR and sensor computer labs.
- 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.
- Expand your network through our excellent teaching links with defence technology-related industry partners such as DSTL, Babcock, Leonardo, BMT, and the Met Office.
Discover Data Science at the University of Exeter.
Top 20 in the UK for Computer Science
Partner to the Alan Turing Institute
Top 10 in the UK for graduate prospects
Joint 9th for graduate prospects for Computer Science in the Complete University Guide 2027
Excellent facilities spanning a wide range of machine types and software ecosystems alongside world-class computer science labs
Entry requirements (typical offer)
| Qualification | Typical offer | Required subjects |
|---|---|---|
| A-Level | AAA - AAB | GCE A-Level Maths grade B. Applicants may offer A-Level Maths, Pure Maths or Further Maths. |
| 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 or C | Grade 4 or 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
This programme is only open to British citizens who qualify as a UK (Home) student for fee purposes.
For further information about eligibility, please contact us.
Course content
This BSc Data Science degree is designed with industry and aimed at students wishing to work or research in data science. It will enable you to develop computer science skills that are increasingly required across the modern defence sector and which support defence applications including autonomous systems, decision-support tools, cyber capability, augmented reality and digitally enabled command environments.
The course will include:
- Core areas of computer science: programming; object-oriented programming; software development; database theory and design
- Core areas of mathematics including discrete maths and probability theory
- Applied data science: machine learning, data structure and algorithm, AI and applications, computational intelligence, HPC, Big Data, Cloud
- Defence context: applications, governance and ethics.
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.
Delivered in collaboration with the defence industry
Defence-related employers will support student projects, placements, guest teaching, hackathons, and career engagement, strengthening the pipeline of graduates with skills relevant to defence, security and advanced technology sectors.
Modules
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 | |
| Defence and Security | 15 | |
| Programming | 15 | |
| Object-Oriented Programming | 15 | |
| Computers and the Internet | 15 | |
| Data Structures and Algorithms | 15 | |
| Discrete Mathematics for Computer Science | 15 | |
| Computational Mathematics | 15 | |
COM1011: Fundamentals of Machine Learning
Differently from traditional software, artificially intelligent software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Artificial Intelligence and Machine Learning. You will learn about the principal paradigms from a theoretical point of view and gain practical experience through a series of workshops. In this module we will emphasize the notion and importance of data and you will learn how machines can deal with different types of data sources, ranging from images and text to networks and user preferences.
Co-requisite Modules: ECM1400, MTH1002, MTH1004, or equivalent.
This module is suitable for students with sufficient preparation in Mathematics and Programming.
This module aims to equip you with the fundamental notions to understand and identify the compromises and trade-offs that must be made when using a machine learning approach. It will provide the foundations to understand the principal flavours of machine learning techniques. Emphasis will be placed on how to work effectively with different information sources.
COM1025: Defence and Security
This module will introduce you to the defence and security world and will highlight the role computing plays within it. You will learn about the structure of the UK defence sector, exploring how different organisations (governmental and commercial) collaborate and leverage computing technology to support national defence and security. You will explore the technical challenges underpinning the defence sector, including artificial intelligence, human factors, quality assurance, security, and resilience. You will have the opportunity to explore these problems in a practical setting, developing solutions to real-world problems arising from the current defence context.
ECM1400: Programming
We use computers in almost all aspects of our daily lives and throughout science, so it is easy to take them for granted. However, in order that we can use computers to solve new problems and create new things, we have to be able to program them. This module introduces you to programming and problem solving with a computer. You will learn how to formulate an algorithm to solve a problem, and you will acquire the skills to write, test and debug programs.
This module is an introductory course in computer programming and will introduce you to the fundamental concepts of computer algorithms and programming, with a strong emphasis on practical implementation. You will also learn how to apply analytical and problem-solving skills to the design and implementation of small applications.
ECM1410: Object-Oriented Programming
This module will introduce you to object-oriented problem-solving methods and provide you with object-oriented (OO) techniques for the analysis, design and implementation of solutions. We will introduce you to these concepts, and you will develop skills with a new programming language. By the end of this module, you will be able to apply these skills to design and implement small applications.
The module aims to provide you with a thorough grounding in the fundamentals of object-oriented design concepts, alongside the fundamentals of the Java programming language, and general object-oriented design concepts. It will also introduce you to widely used components of the unified modelling language (UML), teach you how to interpret and implement a Java program from these higher-level designs, along with the pair programming approach used in industry.
ECM1413: Computers and the Internet
This module is designed to equip you with the foundational information you need to understand and work in business and technical fields requiring the use of computers and networking technologies. Computing technology has a diversity of applications, so this module is suitable both for computer science students and for those pursuing other study disciplines. On this module, you will acquire foundational knowledge of computer systems (operating system and computer architecture) and computer networks.
By the end of the module, you should be well placed to make use of an extensive range of hardware and software technologies. In addition, you will have gained the knowledge and skills to enable you to analyse existing computer- and internet-based information systems.
ECM1414: Data Structures and Algorithms
According to an old formula, Algorithms + Data Structures = Programs. This remains as true today as when it was originally formulated by Niklaus Wirth in 1976, and encapsulates the truism that all computation consists of the manipulation of data by means of systematic procedures. But data comes in many different forms (e.g., numerical, alphabetical, graphical) and only by knowing how it is structured can we specify the procedures – algorithms – for manipulating it to produce desired outcomes. Thus, the study of data structures and algorithms constitutes an integrated topic, which forms the subject matter of this module. You will be introduced to some of the key concepts in the area, with plenty of examples to illustrate them, and you will be given a chance to demonstrate your understanding by undertaking exercises. This module builds on the programming knowledge you have already acquired from ECM1400 Programming and will make use of mathematical tools introduced in ECM1415 (Discrete Mathematics for Computer Science) to enable data structures and algorithms to be described precisely.
Prerequisite module: ECM1400, ECM1415 or equivalent.
ECM1415: Discrete Mathematics for Computer Science
Discrete mathematics is concerned with quantities which vary discretely, and because of that has an important role in Computer Science, in which discrete structures such as sets, graphs, lists, and trees play a fundamental role, and the underlying forms of reasoning are based on propositional and predicate logic rather than on calculus and mathematical analysis, with an emphasis on counting rather than measuring, e.g. enumerating permutations and combinations of objects satisfying specified conditions. This module will provide a thorough grounding in the fundamental structures and methods of discrete mathematics that are required for computer science.
The aim of this module is to provide you with the basic concepts and tools developed in discrete mathematics disciplines but needed for the study of computer science. As such, it forms an essential part of a rounded education of a computer scientist or computer expert whose work includes computer-based data manipulations.
ECM1416: Computational Mathematics
Computer science draws from a wide range of essential mathematical techniques. This module will provide a solid foundation on the required mathematical tools and how to use them in solving computer science problems. This module will introduce linear algebra and vector spaces, statistics and probabilities and numerical optimization. In the course of this module, you will learn to apply theoretical knowledge in concrete programming tasks. This module complements previous mathematics module and is essential for all engaged in a Computer Science program.
In this module we aim to provide you with a foundation in the essential mathematical tools used in advanced computer science topics. We will teach you how to use vector and matrices, statistics and probabilities and numerical optimization methods and implement them in computer programs.
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 | |
COM2011: Machine Learning and Data Science
This module will improve your knowledge and skills in machine learning and data science. You will gain theoretical and practical understanding of some of the core techniques in machine learning (including supervised/unsupervised methods, feature extraction, binary classification, elementary text and image analysis, amongst others). You will also understand how machine learning and other techniques are combined in effective data science workflows, alongside some of the practical challenges faced in real-world data science, such as handling missing or erroneous data, linking different datasets, and data visualisation.
This module is suitable for students with sufficient preparation in Mathematics and Programming.
This module aims to equip you with the fundamentals of machine learning and data analysis. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods. We will pay particular attention to methods for visualising complex datasets.
COM2020: Team Project
This module gives you the opportunity to work collaboratively on a substantial practical problem, which you will solve from a computational and data-based perspective. Teams will apply technical skills in software development and data analysis while managing project planning, teamwork, and communication. During the module you will design and develop a solution that balances innovation with feasibility, and develop a prototype that you demonstrate tackles the project according to specific measures of success. You will develop the professional skills required to succeed in technology and data-driven industries.
The aim of this module is to equip you with the necessary practical and theoretical skills to enable you to develop and implement a computational and data-driven solution to a given problem. Early in the module you will be presented with a realistic problem, and you will be asked to work within a team to propose, develop, and implement a solution to the problem. You will learn how to apply a range of evaluation measures to evaluate the success of your team’s solution. Throughout the module you will learn about and deploy teamwork skills to ensure the success of your project.
ECM2414: Software Development
The module will introduce you to software design and development concepts and methods, alongside intermediate and advanced constructs and concepts in the Java programming language, and the programming paradigms these relate to. This includes generic programming (and Java generics), concurrent programming (via Java threads), design patterns, networked programs and nested inner classes. We will also cover widespread tools in software development, including version control and unit testing.
This module will introduce you to methods for the rigorous testing and assessment of software, and prepare you for complex programming tasks in a specific object-oriented programming language, including advanced concepts and syntax, and the use of multiple programs in parallel.
ECM2419: Database Theory and Design
This module will give you an insight into the theoretical and technical issues underlying current and future database management systems. You will acquire practical and theoretical competence in database modelling and design, as well as gaining familiarity with modern state-of-the-art database technology.
Prerequisite module: ECM1400, ECM1410, ECM1413 or equivalent.
The intention of the module is to equip you with the theoretical and practical knowledge needed to design, develop and manage database systems using modern database management systems. You will get hands-on experience on a selected database management system that is currently in commercial use. By the end of the module you will be competent with the methods for designing, developing and managing database systems and their associated forms-based applications.
MTH2006: Statistical Modelling and Inference
Statistical modelling lies at the heart of modern data analysis, helping us to describe and predict the real world. Statistical inference is the way that we use data and other information to learn about and apply statistical models. In this module, you will learn the theory underpinning modern statistical methods such as fitting normal linear models, evaluating how well they fit the data and taking inferences from it. You will apply the theory using statistical software such as R to analyse and draw conclusions from a range of real-world data sets. Topics covered in the module range from estimators, confidence intervals, design of experiments and hypothesis testing to statistical modelling, regression, inference and comparison of models. Skills developed in the module are taken further in modules such as MTH3012 Advanced Statistical Modelling.
This module aims to develop understanding and competence in statistical modelling by introducing you to the Normal linear model from a modern perspective. It will provide you with the ability to formulate and apply these models in a range of practical settings, to carry out associated inference appreciating how this relates to the general likelihood inferential framework, and to perform appropriate model selection and model checking procedures. Use will be made of a suitable statistical computer language for practical work.
SPA2009: Data Science in Society
This module will focus on the societal context for data science, machine learning and artificial intelligence. An increasing number of social, governmental and commercial processes now take place in online or digital environments, making it important to consider the ways in which data is used to make decisions and how the application of computational methods to engineer social processes can be managed in ways that are ethical, transparent and socially acceptable. This module will teach you the core knowledge around data ethics, privacy, fairness and data governance. You will be encouraged to form your own opinions on how digital tools can best be developed to deliver benefits and avoid harm. Seminar discussions and ethical case studies will be used to highlight and explore different social issues around data science.
Suitable for non-specialists and interdisciplinary pathways.
This module aims to:
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Computational Intelligence | 15 | |
| Artificial Intelligence and Applications | 15 | |
| Outside the box: Computer Science Research and Applications | 15 | |
COM2014: Computational Intelligence
Computational intelligence is the science of computational systems that are able to perform specific tasks, adapting to particular data. The module will equip you to design and use computational intelligence to solve a variety of problems such as planning, scheduling, optimisation, using a variety of techniques including biologically inspired computational, fuzzy logic, agent-based models and simulation.
Pre-requisite Modules: COM2013 (Data Science Group Project 2); ECM1400; MTH1004
The aim of this module is to introduce and give you practice in some of the main areas of computational intelligence that can be used to solve problems arising in data science. It aims to give you and understanding of the theoretical basis of these methods and their relation to other artificial intelligence techniques. Specifically, it will introduce classical “crisp” logic and knowledge representation before proceeding to fuzzy logic to cope with uncertain and vague processes. Searching and optimisation arise in many contexts and this module aims to introduce you deterministic and stochastic optimisation methods, particularly evolutionary optimisation.
ECM2423: Artificial Intelligence and Applications
Artificial Intelligence is the science of getting computers to do things which, when done by humans, involve the exercise of intelligence. It has been an important strand of Computer Science throughout the lifetime of that discipline, and has exerted a significant influence on other areas of Computer Science as well as on practical applications. This module will provide you with a broad overview of Artificial Intelligence, as well as a more detailed understanding, both practical and theoretical, of selected topics within this area. This module is suitable for any student who has a basic knowledge of computer programming, as well as linear algebra, discrete mathematics, and probability theory.
Pre-requisites: ECM1415 and ECM2418
In this module we aim to provide you with a general introduction to some of the main topics within the broad field of Artificial Intelligence, beginning with an overview of the history and philosophy of AI, then proceeding to a more detailed examination of a range of specific sub-areas, including logic and knowledge representation, searching algorithms, machine learning, and natural language processing.
ECM2427: Outside the box: Computer Science Research and Applications
This module gives you a chance to explore the breadth and depth of Computer Science beyond the core technical content of the main syllabus, and to investigate current research in Computer Science and how it is used to solve problems in other areas. It will explore some of the frontiers of research in the department and, through lectures by specialists in other fields, will introduce you to some of the uses of Computer Science methods in business, the sciences, social sciences and humanities.
This module aims to introduce students to current Computer Science beyond the confines of the main syllabus. On one side, it will introduce you to some of the research into new ideas in Computer Science, and on the other, it will explore some applications where Computer Science is essential. You will learn about the nature and purpose of research, some current research problems, the methods employed to tackle them, and how the results are evaluated. You will also learn about some of the ways existing Computer Science techniques and technologies are applied to solve problems outside Computer Science, particularly large-scale computing applications.
You will demonstrate what you have learnt by producing an in-depth review on one of the topics covered by the seminars, and in groups, you will also find out about a current topic of Computer Science and technology and make a presentation on it.
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 | |
COM3021: Data Science at Scale
Data science relies on large amounts of data to be effective and many commercial and scientific applications require the analysis of large quantities of heterogenous, noisy data on distributed machines. This module will examine the ways in which algorithms for data science can be implemented for large data and will discuss new algorithms specifically designed for large scale data. You will also work with large-scale distributed and cloud systems for storing and computing with big data.
Through theory and practice this module aims to equip you with an understanding of the principles of distributed computing, particularly on cloud-based systems, the ways in which data can be stored and accessed to allow efficient computation, and efficient algorithms for large-scale computation.
Distributed cloud computing will provide you with the underpinning knowledge required to develop and implement machine learning and artificial intelligence algorithms on distributed high-performance computing systems.
COM3031: Probabilistic Machine Learning
This module provides an advanced exploration of machine learning and artificial intelligence, focusing on probabilistic modeling, inference techniques, and structured learning methods. It also examines key theoretical foundations alongside advanced techniques, such as Bayesian Neural Networks and Variational Autoencoders, which enable uncertainty quantification and probabilistic generative modeling.. The module delves into Bayesian theory, its role in handling uncertainty, and its connections to approximate inference methods and information theory. Students will also explore techniques for modeling temporally and spatially structured data, including Hidden Markov Models. Additionally, the module introduces reinforcement learning. By integrating probabilistic reasoning, approximate inference, and structured learning, this module equips students with the theoretical depth and practical skills required for tackling complex machine learning problems.
ECM3401: Individual Literature Review and Project
This is the module in which everything you have learnt in your Computer Science studies comes together in a substantial piece of individual project work, involving initial research and literature review, and specification and design of a software system, followed by implementation, testing, evaluation, and demonstration of the system. You will work under the supervision of an individual staff member who will provide guidance and advice as appropriate.
The aim of the module is to enable you to consolidate the knowledge, understanding, techniques and skills acquired over the previous two years through the specification, design, implementation, testing, evaluation and demonstration of a software system. The module includes both initial research into the project area (including production of a literature review) and production of the system itself following an appropriate development method.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Computer Vision | 15 | |
| Social Networks and Text Analysis | 15 | |
| Digital Twins and Simulation | 15 | |
| Geospatial AI | 15 | |
| Immersive Computing | 15 | |
| Edge Computing | 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 | |
£6.4 million investment to expand computing and defence-related skills
The University of Exeter has secured £6.4 million in Government funding to expand computer science and advanced engineering education, supporting the development of the next generation of highly skilled graduates needed by the UK’s defence, security and technology sectors.
It will support opportunities for students to develop skills in areas critical to the UK’s future prosperity, resilience and security, including advanced manufacturing, infrastructure, digital technology, energy, healthcare, transport, security and defence.
Our new, defence-focused computer science degree pathways include enhanced practical teaching and stronger employer engagement within the defence sector.
AR, VR, XR and sensor labs
Learning and teaching will be supported through two new computer science laboratories, one of which focuses on augmented, virtual and extended reality (AR/VR/XR) while the other provides access to sensor and edge computing technologies. These facilities will support hands-on teaching and access to specialist practical learning aligned with emerging defence technologies.
Delivered in collaboration with the defence industry
The new defence-focused computing pathways will be delivered with defence employers, industry partners, and education providers. Employers will support student projects, placements, guest teaching, hackathons, and career engagement, strengthening the pipeline of graduates with skills relevant to defence, security and advanced technology sectors.
Fees
Tuition fees for 2026 entry
UK students: £9,790 per year
This programme is only open to British citizens who qualify as a UK (Home) student for fee purposes.
For further information about eligibility, please contact us.
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.
World-class facilities
Our latest computing facilities are world-class spacious teaching labs allowing comfortable, collaborative working in a sensory-friendly environment.
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.
Assessment
Modules are assessed by a combination of continuous assessment through small practical exercises, project work, practical development tasks, report writing, presentations and exam.
Employer engagement
Employers will support student projects, placements, guest teaching, hackathons, and career engagement, to help you develop skills relevant to defence, security and advanced technology sectors.
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.
Supportive community
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. The department has a student-focused approach to teaching, whereby all members of staff deal with questions on an individual basis. 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.
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.
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.
Your future
Develop skills for the defence industry
Computer Science courses including Data Science, Artificial Intelligence and Computer Science develop the skills that are increasingly required across modern defence systems. You will gain expertise in areas including artificial intelligence, data analysis, distributed systems, sensing technologies and complex systems modelling.
These capabilities support defence applications including autonomous systems, decision-support tools, cyber capability and digitally enabled command environments.
Engage with defence employers
Industry engagement ensures that course content remains aligned with defence sector requirements. Defence partners contribute to teaching through guest lectures, collaborative projects and industry-informed modules - helping you to gain valuable sector understanding, as well as developing your career network.
Demand from defence employers demonstrates the relevance of this course. Defence companies including Babcock, Northrop Grumman, Leonardo, MBDA, Supacat, Synoptix and FNC, as well as a range of SMEs, engage with Exeter through careers activity and graduate recruitment pipelines.
Over the past 12 months* more than forty defence organisations have advertised opportunities or participated in employer engagement activity with University of Exeter students.
Career paths
The broad-based skills acquired during your degree will also give you an excellent grounding for a wide variety of careers, not only those related to the defence sector but those in wider fields too.
Wider career opportunities for data scientists are limitless. There is an established strong market demand for suitably skilled data scientists and data science skills are increasingly being sought across sectors beyond defence, particularly by the finance and accounting industries, supermarkets, online retailers such as Amazon, and the NHS.
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 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.
Examples of roles recent graduates from our Computer Science department are now working in include:
- Analytics Manager
- Business Intelligence
- Analyst
- Business Statistician
- Data Analyst
- Data Architect
- Data Scientist
- Machine Learning
- Engineer
- Quantitative Researcher
- Research Analyst
- Research Scientist
Careers and employability support
Our staff are active in developing our programmes and services to improve the employability of our students. We also have a dedicated Careers Consultant who provides career workshops tailored to computer science, as well as support in job applications and interview skills.
The Career Zone run several careers fairs throughout the year which are particularly successful in putting major UK employers in touch with Exeter students. Relevant employers visit the department from the first year to meet and hold mock interviews with students, helping you to develop your career ideas at an early enough stage to help with module choices and placement decisions.
*2025/26
