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Study information

Biomedical Data and Artificial Intelligence (2023)

1. Programme Title:

Biomedical Data and Artificial Intelligence

NQF Level:


2. Description of the Programme (as in the Business Approval Form)

The MSc Biomedical Data and Artificial Intelligence programme at the University of Exeter provides the quantitative skills required to analyse and understand data arising in biology, medicine and beyond. These data, which may represent the outputs of experiments or clinical recordings, are diverse and challenging to interpret. We therefore require advanced analysis tools and mathematical models to understand the underlying physical principles that generate these data. The combination of advanced data analysis and predictive modelling that you will encounter in this programme is applicable beyond the life and medical sciences, into other areas such as finance and climate systems. 
The degree is taught by a community of researchers at Exeter who are mathematically trained but whose research crosses disciplinary boundaries into experimental biology as well as biological and medical application areas. We are based in the Living Systems Institute, a setting that co-locates researchers across biology, medicine, mathematics and physics. This provides a unique opportunity to study at the forefront of data analysis, AI and modelling applied to real-world applications. The research projects that you undertake will arise from current problems in biology and medicine and will be co-supervised by experts in those fields. 

3. Educational Aims of the Programme

The programme will provide training in mathematical and computational methods to quantify data, construct predictive models, and to link models with data. The core modules are led by experts in the analysis and modelling of biological and medical data. The content is centred on real-world applications: for example, analysis and modelling of motility in single cell organisms, clinical time series in neurology and neuroendocrinology as well as analysis of next generation sequencing data. In particular, the programme will introduce you to: 
- Fundamental data analysis methods pertinent for biomedical data.
- Principles of building and analysing predictive models of biological systems.
- Methods to compare models to data, thereby inferring underlying biophysical mechanisms. 
Through optional modules you will be able to deepen your understanding of the underpinning mathematics, statistics or computational methodologies. In addition, the programme will 
develop research skills, professional and communication skills and core academic skills that will prepare you for a wide range of employment opportunities. These include preparation for further research such as going onto further study for a PhD.

4. Programme Structure

Your  programme is a (1) year programme of study at National Qualification Framework (NQF) level (7) (as confirmed against the FHEQ). This programme is divided into (1) ‘Stages’. Each Stage is normally equivalent to an academic year.  The programme is also 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.

Exit Awards

If you do not complete the programme you may be able to exit with a lower qualification.

Postgraduate Diploma: At least 120 credits of which 90 or more must be at NQF level 7.

Postgraduate Certificate: At least 60 credits of which 45 or more must be at NQF level 7.

5. Programme Modules

The following tables describe the programme and constituent modules. Constituent modules may be updated, deleted or replaced as a consequence of the annual programme review of this programme. Details of the modules currently offered may be obtained from the College web site

You may take Option Modules as long as any necessary prerequisites have been satisfied, where the timetable allows and if you have not already taken the module in question or an equivalent module. Descriptions of the individual modules are given in full on the College web site


Stage 1

Code Title Credits Compulsory NonCondonable
MTHM021Advanced Mathematics Project60YesYes
MTHM007Engaging with Research15YesNo
MTH3006Mathematical Biology and Ecology15YesNo
MTHM009Advanced Topics in Mathematical Biology15YesNo
NSCM005Mathematical Modelling in Biology and Medicine15YesNo
MTHM015AI and Data Science Methods for Life and Health Sciences15YesNo
Select 45 Credits:
MTHM017Advanced Topics in Statistics15NoNo
MTHM018Dynamical Systems and Chaos15NoNo
MTHM033Statistical Modelling in Space and Time15NoNo
MTHM503Applications of Data Science and Statistics15NoNo
MTH3039Computational Nonlinear Dynamics15NoNo
MTH3008Partial Differential Equations15NoNo
MTH3011Nonlinear Systems and Control15NoNo
MTH3024Stochastic Processes15NoNo
MTH3028Statistical Inference: Theory and Practice15NoNo
MTH3027Special Topics in Statistics 15NoNo
MTH3049An Introduction to Causal Inference15NoNo
MTHM***NQF Level 7 modules in Mathematics15NoNo
ECMM4**NQF Level 7 modules in Computer Science15NoNo
MTH3***NQF Level 6 modules in Mathematics15NoNo

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 & Knowledge

A Specialised Subject Skills & Knowledge
1. Employ a range of techniques for quantifying biomedical data.
2. Develop and analyse predictive models of biological systems. 
3. Be able to quantitatively link models and data, thereby inferring plausible underlying mechanisms. 

Learning & Teaching Activities

Lectures, seminars, examples classes, tutorials, practical computer workshops, literature discussion sessions, formative exercises, individual supervision

Assessment Methods

Written coursework, class tests, written examinations

B Academic Discipline Core Skills & Knowledge

1. Effectively handle large and complex data sets. 
2. Critically analyse and interpret relevant academic and technical literature. 
4. Integrate theory and applications.
5. Transfer knowledge and methods from one subject area to a different area.
6. Effectively use a range of computer packages for analysing data and models and for producing publications and presentation quality graphical output.

Learning & Teaching Activities

Lectures, seminars, examples classes, tutorials, practical computer workshops, literature discussion sessions, formative exercises, individual supervision

Assessment Methods

Written coursework, class tests, written examinations

C Personal / Transferable / Employment Skills & Knowledge

1. Effectively communicate methods and results based on analysis of complex datasets in both written reports and oral presentations.
2. Communicate effectively with people from different disciplines. 
3. Write up a sustained piece of research work in a coherent and logical dissertation.
4. Give interesting and informative oral presentations on high-level scientific research topics.
5. Use libraries, databases and the web effectively for research.
6. Work effectively independently or as part of a team.
7. Plan career and personal development.


Learning & Teaching Activities

Practical computer workshops, literature discussion sessions, individual supervision

Assessment Methods

Written coursework, dissertation, oral 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. You can also make an appointment to see individual teaching staff.

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 College 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 College and an extensive range of web-based information (see ) is maintained for the use of students, including a comprehensive and annually revised student handbook.

The Harrison Learning Resource Centre is generally open during building open hours. The Centre is available for quiet study, with four separate rooms that can be booked for meetings and group work. Amongst its facilities, the Learning Resource Centre has a number of desks, four meeting rooms with large LCD screens, and free use of a photocopier. Also available are core set texts from your module reading lists, and undergraduate and MSc projects from the past two years.

Online Module study resources provide materials for modules that you are registered for, in addition to some 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 (

Student/Staff Liaison Committee enables students & staff to jointly participate in the management and review of the teaching and learning provision.

10. Admission Criteria

All applications are considered individually on merit. The University is committed to an equal opportunities policy with respect to gender, age, race, sexual orientation and/or disability when dealing with applications. It is also committed to widening access to higher education to students from a diverse range of backgrounds and experience.
Candidates must satisfy the general admissions requirements of the University of Exeter.
Candidates will be required to have at least a 2:2, or equivalent in a quantitative discipline (e.g. mathematics, computer science, engineering, etc.). Where possible students will be interviewed before admission. Overseas students without English as a first language must show proficiency in English and have an appropriate qualification.

11. Regulation of Assessment and Academic Standards

Each academic programme in the University is subject to an agreed College 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 College 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 College of Engineering, Mathematics and Physical Sciences
16 Partner College / Institution
17 Programme accredited/validated by
18 Final Award(s) MSc
19 UCAS Code (UG programmes) mathmodbio
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 180 credits (90 ECTS)
22 QAA Subject Benchmarking Group (UG and PGT programmes) Mathematics, Statistics and Operational Research
23 Origin Date February 8th 2023 Last Date of Revision: April 24th 2023