MSc Advanced Data Science
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | 1234 |
|---|---|
| Duration | 1 year full time |
| Entry year | 2026 + January 2027 start |
| Campus | Streatham Campus |
| Typical offer | 2:1 degree in Computer Science, Data Science or any other strongly numerate discipline |
|---|---|
Why study MSc Advanced Data Science at Exeter?
- MSc Advanced Data Science is an ideal course for you if you have a computational or mathematical background* and wish to build on your existing knowledge whilst learning to apply your skills to complex contemporary problems across the breadth of society.
- Our flexible programme gives you the choice to advance your study in core data science skills as well as artificial intelligence and machine learning.
- A business-linked, research-led programme which focusses on the latest techniques and technologies, getting you ready to work at the cutting edge.
- Taught by leading academics in the fields of artificial intelligence, machine learning, data science, high performance computing, and cyber-security.
- You will be immersed in a community of researchers working at the forefront of modern technological developments, with excellent facilities spanning a wide range of machine types and software ecosystems.
- Take advanced courses that prepare you for research, ideal for those considering future PhD study.
- Our latest facilities for computer science students are world-class spacious teaching labs allowing comfortable, collaborative working in a sensory-friendly environment fully supported by your teachers.
*If you are interested in an MSc in data science but do not meet the entry requirements for this programme, you are welcome to apply for MSc Data Science.
Fast Track (current Exeter students)
Contact
Programme Directors: Dr Rudy Arthur (September), Dr Federico Botta (January)
Web: Enquire online
Phone: +44 (0)1392 72 72 72
Top 20 for Computer Science
20th in The Times and The Sunday Times Good University Guide 2024
Top 10 in the UK for graduate prospects
Joint 9th for graduate prospects 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
The University has invested £50 million in the development of its Data Science and Artificial Intelligence capabilities
Opportunity to attend advanced research seminars in Computer Science, the Institute of Data Science and Artificial Intelligence and other research institutes
"The University was very appealing for me as it is a long-standing partner with the Alan Turing Institute and home to the Institute for Data Science and AI. The course has been very well designed as it enhanced my research and presentation capabilities. Module leaders are very helpful and possess expertise in their field."
"There are wellbeing services available at the university for students facing challenges. I contacted my personal tutor and computer science pastoral mentor quite a few times, and they were very helpful and checked in with me regularly throughout. The university has a Career Zone that provides a lot of opportunities for student to polish their soft skills and offers career advice when looking for graduate jobs/ internships."
Hamza
MSc Advanced Data Science
Entry requirements
2:1 degree in Computer Science, Data Science or any other strongly numerate discipline such as Maths, Statistics, Physics or other degrees with a focus on computation. Students must have evidence of programming and mathematics modules and/or quantitative research experience on transcript.
We may consider applications with non-standard qualifications where there is evidence of exceptional performance in modules relevant to the programme of study and significant relevant work experience, or relevant professional qualifications.
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.
Course content
This advanced master’s programme links to our internationally leading research in Data Science, Machine Learning and AI. It focuses on the latest techniques and technologies, teaching you how to apply these to a breadth of complex problems.
You will engage with research informed teaching across a range of data science topics, including, artificial intelligence, machine learning, machine vision, network science, statistics and more.
Individual and group research projects will allow you to apply and demonstrate the skills you learn to build a portfolio of work, preparing you for PhD study or professional data science roles.
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.
60 credits of compulsory modules, 120 credits of optional modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Research Project | 60 | |
COMM514: Research Project
In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results.
Research topics can be selected from across the breadth of computer science, data science and related topics. The project may include theoretical analysis, as well as practical software implementation.
This module aims to give you in-depth experience of research in an area relating to your programme of study. It will help you prepare for projects both in an industry or commercial setting, as well as in further postgraduate research work, such as a PhD. The module builds 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 and presentation of results.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Network Science | 15 | |
| Text Mining and Natural Language Processing | 15 | |
| Deep Learning | 15 | |
| Data Science at Scale | 15 | |
| Generative AI Applications | 15 | |
| Large Language Models and Applications | 15 | |
| Nature-Inspired Computation | 15 | |
| Machine Learning | 15 | |
| Evolutionary Computation and Optimisation | 15 | |
| Computer Vision | 15 | |
| Statistical Modelling in Space and Time | 15 | |
| Bayesian Philosophy and Methods in Data Science | 15 | |
| Data Governance and Ethics | 15 | |
Please note that the module information displayed here is subject to change.
180 compulsory credits.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Network Science | 15 | |
| Text Mining and Natural Language Processing | 15 | |
| Deep Learning | 15 | |
| Data Science at Scale | 15 | |
| Research Project | 60 | |
| Machine Learning | 15 | |
| Computer Vision | 15 | |
| High-Performance Computing | 15 | |
| Data Governance and Ethics | 15 | |
COMM039: Network Science
Many of the most important datasets are relational: friends and followers on social media, users who buy similar products, towns connected by roads, computers connected by routers and so on. Network Science is how we study and understand large relational data sets. In this module you will study how to represent, visualise, summarize and analyse large networks to learn about communities, epidemics, transport and resilience in real systems. This module is appropriate for students interested in data science and requires some programming and math background.
This module aims to give students the expertise to model and analyse large network datasets. After a grounding in the basics of network science we move on to more advanced techniques and algorithms to perform rigorous analysis of large networks and showing how these methods can be applied to real data sets.
COMM040: Text Mining and Natural Language Processing
Text mining is the process of extracting insight from large collections of written documents. Recently, there has been immense progress in how computers understand human language. This means reviews, tweets, archives of legal documents, recipes and all kinds of text can now be effectively analysed. This module teaches you how to search, group, summarise and understand large corpuses of documents. The course will cover methods like topic modelling, sentiment analysis, translation and the use of Large Language Models to solve real world problems. The student should have taken or be taking a module on the basics of machine learning.
Students will understand and apply modern NLP methods to real world textual datasets. The focus will be on methods for generating insight from large collections of text, from practical first steps, like data cleaning and validation, to topic modelling using a variety of cutting-edge techniques.
COMM113: Deep Learning
Deep Learning is a highly in-demand skill in AI. In this module, you will study foundational and advanced deep learning techniques, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn key concepts, including, for example, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and contemporary advancements such as Transformers, with practical applications across various domains. You will attend lectures providing in-depth coverage of theories and algorithms. In addition, you will attend lab sessions where you'll apply theoretical concepts to hands-on practices. This module is suitable for Computer Science, Mathematics and Engineering students and any students with experience in programming and foundational machine learning concepts.
COMM115: Data Science at Scale
Data science and some machine learning technologies rely 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.
ECMM422: Machine Learning
Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition. Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.
In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.
ECMM426: Computer Vision
How do we recognise objects and people? How can we catch a ball or navigate a busy room without collisions? These everyday tasks have challenged AI scientists for decades. Recent advances in computer vision have led to major improvements in applications such as face detection, body tracking, autonomous vehicles, and action recognition.
This module introduces the fundamentals of computer vision, covering both classical and state-of-the-art methods. You will gain a theoretical understanding of key algorithms, along with practical skills in image processing, feature extraction, object detection, segmentation, and deep learning for vision tasks. The course also explores 3D vision and modern topics such as video analysis and low-shot learning, providing a broad foundation for solving real-world vision problems.
ECMM461: High-Performance Computing
The demand for ever-increasing computational power drives the development and exploitation of high-performance computing that underpins leading edge research in computationally intensive engineering technologies fields. This module is designed to equip you with a solid foundation and useful skills in high-performance and distributed computing. In this module, you will learn about current high-performance computer architectures and how the computer architecture influences the performance of algorithms and programs. You will also develop skills in parallel algorithm design and parallel programming, and will gain experience of using a high-performance computing system.
This module aims to provide you with a thorough grounding in parallel programming and the architectures used in high-performance computing. After presenting the fundamental ideas and basic concepts of high-performance computing, the module outlines the architectures, components and parallel programming of high-performance computers. The module will introduce you to recent developments and future trends in architecture and algorithms in high-performance computing.
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.
Fees
2026/27 entry (including January 2027 entry)
UK fees per year:
£14,300 full-time
International fees per year:
£30,300 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.
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.
Teaching and research
All of our courses are taught by active researchers who work closely with industrial partners. Our module leads are renowned in their field with prestigious fellowships and awards, many also consulting with major companies. Teaching is mainly delivered by lectures, workshops and online materials. You will have access to comprehensive online learning materials through the University’s virtual learning environment, including recorded lectures. Each module references core and supplementary texts, or material recommended by module deliverers, which provide in depth coverage of the subject and go beyond the lectures.
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. Find out about our data science researchers by discovering our hub for data-intensive science and artificial intelligence at the Institute for Data Science and Artificial Intelligence (IDSAI). You can also read more about our current computer science research.
Investment in data science and artificial intelligence
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.
Supportive environment
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. 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.
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.
Careers
The need for data scientists continues to grow alongside the huge amount of online, sensor and other data generated daily. A modern data scientist needs to have a wide range of skills, from data visualisation to cloud computing to advanced AI and machine learning, in order to turn this mountain of data into business insight. As the field advances and the number of data scientists grows, the learning and practical experience you will develop on this course will make you stand out from the crowd.
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 Data Science. Examples of graduate roles include data scientist, machine learning scientist, Python programmer and software engineer.
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.
