Advanced Data Science (2025)
1. Programme Title:Advanced Data Science |
NQF Level: |
7 |
|---|
2. Description of the Programme (as in the Business Approval Form) |
|---|
This is an advanced Masters programme, linked to internationally leading research in Data Science, Machine Learning and AI. This programme is more technically challenging than most Data Science MSc degrees, making it ideal for students with some prior experience to develop their expertise and work at the cutting edge of Data Science. On this degree you will be able to engage with research informed teaching across a range of Data Science topics from leading academics. These topics include artificial intelligence, machine learning, machine vision, natural language processing, network science, statistics and more. This MSc will prepare you for PhD level study in Data Science and give you the technical and non-technical skills (communication, team-working, project management) to enhance your employability in Industry. Individual and group research projects will allow you to apply and demonstrate the skills you learn to build a portfolio of work. Data Science is a growth area with excellent career development potential. The University of Exeter is a world class research-active institution which regularly features in UK Top-10 and Global Top-100 rankings. The University is making significant new investment in Computer Science and Data Science. |
3. Educational Aims of the Programme |
|---|
The course will provide the highest standard of Data Science training at MSc level. Students will learn modern Data Science methods including machine learning, statistical modelling, big data, image and text analysis as well as the mathematical and computational concepts underpinning them. Students will learn to apply these methods in realistic scenarios to prepare them for research or work in industry. Content will be delivered through lectures, workshops, project work and self-study on Exeter’s Streatham campus. |
4. Programme Structure |
|---|
MSc Advanced Data Science is a 1-year full-time programme of study at Regulated Qualifications Framework (RQF) level 7 (as confirmed against the FHEQ). Your programme is divided into units of study called ‘modules’ which are assigned a number of ‘credits’. The credit rating of a module is proportional to the total workload, with 1 credit being nominally equivalent to 10 hours of work. You will need to complete a total of 180 credits across your 12-month programme. This will be a mix of mandatory and optional modules, subject to change and timetabling requirements. Interim / Exit Awards Faculty to provide details for this specific programme. Note: Guidance on Interim and Exit awards (and the difference between than can be viewed here: https://www.exeter.ac.uk/v8media/specificsites/tqa/artmap/Chapter_1.pdf If you do not complete the programme, you may be able to exit with a lower qualification. A Postgraduate Diploma may be awarded when a student gains at least 120 credits from the compulsory modules. A Postgraduate Certificate may be awarded when a student gains at least 60 credits from the compulsory modules |
5. Programme Modules |
|---|
Stage 1
| Code | Title | Credits | Compulsory | NonCondonable |
|---|---|---|---|---|
| COMM514 | Research Project | 60 | Yes | Yes |
| COMM039 | Network Science | 15 | No | No |
| COMM115 | Data Science at Scale | 15 | No | No |
| ECMM409 | Nature-Inspired Computation | 15 | No | No |
| MTHM033 | Statistical Modelling in Space and Time | 15 | No | No |
| COMM113 | Deep Learning | 15 | No | No |
| SOCM033 | Data Governance and Ethics | 15 | No | No |
| ECMM423 | Evolutionary Computation & Optimisation | 15 | No | No |
| ECMM426 | Computer Vision | 15 | No | No |
| COMM040 | Text Mining and Natural Language Processing | 15 | No | No |
| COMM116 | Generative AI Applications | 15 | No | No |
| COMM117 | Large Language Models and Applications | 15 | No | No |
| ECMM422 | Machine Learning | 15 | No | No |
| MTHM047 | Bayesian Statistics, Philosophy and Practice | 15 | No | No |
60 credits of compulsory modules, 120 credits of optional modules.
6. Programme Outcomes Linked to Teaching, Learning & Assessment Methods |
||||
|---|---|---|---|---|
| On successfully completing the programme you will be able to: | Intended Learning Outcomes (ILOs) will be accommodated & facilitated by the following learning & teaching and evidenced by the following assessment methods: | |||
A Specialised Subject Skills & KnowledgeOn successfully completing the programme you will be able to: Specialised Subject Skills and Knowledge: 1. Understand, implement and apply methods of statistical modelling and machine learning. 2. Work with a variety of data types such as: text, images, geographic data, network data and more. 3. Construct, manage and improve data pipelines for specific end-use cases 4. Understand the social and ethical implications of data analysis and how to guard against bias. 5. Rigorously evaluate the uncertainty, errors and biases of statistical and machine learning methods. | Learning & Teaching ActivitiesIntended Learning Outcomes (ILOs) will be accommodated and facilitated by the following learning and teaching and evidenced by the following assessment methods:
Learning and Teaching activities (in/out of class)
Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.
| |||
Assessment MethodsThe assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods will include essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.
| ||||
B Academic Discipline Core Skills & KnowledgeAcademic Discipline Core skills and Knowledge 6. Collect, clean, parse, store and summarise large data sets. 7. Discover patterns in and relationships between data sets. 8. Work with a variety of hardware and computing environments. 9. Understand, apply and develop the principles and techniques underlying data science and machine learning methods. 10. Read, interpret, apply and contribute to academic literature in data science and machine learning. 11. Analyse data within the appropriate legal and regulatory frameworks, applying principles of data privacy and data governance. | Learning & Teaching ActivitiesLearning and Teaching activities (in/out of class) Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures. | |||
Assessment MethodsThe assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods will include essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations | ||||
C Personal / Transferable / Employment Skills & KnowledgePersonal/ Transferable/Employment Skills and Knowledge 12. Use data to gain insight to real world problems or extract business value. 13. Work individually or as part of a team on complex software and data science projects. 14. Scope and plan small, medium and large data science projects. 15. Discuss and present the results of an analysis using appropriate communication and visualization. 16. Advise non-technical stakeholders on the application and use of data science, machine learning and AI methods. | Learning & Teaching ActivitiesLectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures. | |||
Assessment MethodsThe assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods will include essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations. | ||||
7. Programme Regulations |
|---|
Full details of assessment regulations for all taught programmes can be found in the TQA Manual, specifically in the Credit and Qualifications Framework, and the Assessment, Progression and Awarding: Taught Programmes Handbook. Additional information, including Generic Marking Criteria, can be found in the Learning and Teaching Support Handbook. |
8. College Support for Students and Students' Learning |
|---|
In accordance with University policy, a system of personal tutors is in place for all students on this programme. A University-wide statement on such provision is included in the University's TQA Manual. As a student enrolled on this programme, you will receive the personal and academic support of the Programme Coordinator and will have regular scheduled meetings with your Personal Tutor; you may request additional meetings as and when required. The role of personal tutors is to provide you with advice and support for the duration of the programme and extends to providing you with details of how to obtain support and guidance on personal difficulties, such as accommodation, financial difficulties and sickness. In addition to this, a Pastoral Mentor is also available for all students on this programme to engage with. The Pastoral Mentor can offer further support around wellbeing and academic concerns, and offers regular opportunities for students to engage with them, such as bookable meetings. You can also make an appointment to see individual teaching staff. Student/Staff Liaison Committee enables students & staff to jointly participate in the management and review of the teaching and learning provision. Online Module study resources provide materials for modules that you are registered for, including recording of lectures, as well as useful subject and IT resources. Generic study support resources, library and research skills, past exam papers, and the 'Academic Honesty and Plagiarism' module are also available through the student portal. Information Technology (IT) Services provide a wide range of services throughout the Exeter campuses including open access computer rooms, some of which are available 24 hours, 7 days a week. Help may be obtained through the Helpdesk, and most study bedrooms in halls and flats are linked to the University's campus network. Additionally, the department has its own dedicated IT support staff, helpdesk and computer facilities which are linked to the wider network, but which also provide access to some specialised software packages. Email is an important channel of communication between staff and students in the department and a student handbook is available with relevant information for your studies. The department has dedicated access to two new state-of-the-art computer labs (Lovelace and Babbage), where practical sessions of modules are typically held and that students can also use for self-studying and group work. |
10. Admission Criteria |
|---|
Undergraduate applicants must satisfy the Undergraduate Admissions Policy of the University of Exeter. Postgraduate applicants must satisfy the Postgraduate Admissions Policy of the University of Exeter. Specific requirements required to enrol on this programme are available at the respective Undergraduate or Postgraduate Study Site webpages. |
11. Regulation of Assessment and Academic Standards |
|---|
Each academic programme in the University is subject to an agreed Faculty assessment and marking strategy, underpinned by institution-wide assessment procedures.
The security of assessment and academic standards is further supported through the appointment of External Examiners for each programme. External Examiners have access to draft papers, course work and examination scripts. They are required to attend the Board of Examiners and to provide an annual report. Annual External Examiner reports are monitored at both Faculty and University level. Their responsibilities are described in the University's code of practice. See the University's TQA Manual for details.
|
12. Indicators of Quality and Standards |
|---|
Certain programmes are subject to accreditation and/or review by professional and statutory regulatory bodies (PSRBs). |
| 14 | Awarding Institution | University of Exeter | |
|---|---|---|---|
| 15 | Lead College / Teaching Institution | Faculty of Environment, Science and Economy | |
| 16 | Partner College / Institution | ||
| 17 | Programme accredited/validated by | ||
| 18 | Final Award(s) | MSc | |
| 19 | UCAS Code (UG programmes) | ADVDATSCI | |
| 20 | NQF Level of Final Awards(s): | 7 | |
| 21 | Credit (CATS and ECTS) | 180/90 | |
| 22 | QAA Subject Benchmarking Group (UG and PGT programmes) | Computing | |
| 23 | Origin Date | November 28th 2024 | Last Date of Revision: | August 19th 2025 |
|---|


