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Study information

Programme Specification for the 2023/4 academic year

MSc Health Data Science (P/T 2 year)

1. Programme Details

Programme nameMSc Health Data Science (P/T 2 year) Programme codePTS2EMSEMS06
Study mode(s) Academic year2023/4
Campus(es)St Luke's (Exeter)
NQF Level of the Final Award7 (Masters)

2. Description of the Programme

As an Exeter Health Data Science MSc student you will:

  1. Be participating in one of only 6 new MSc courses in the UK funded by Health Data Research UK (HDRUK) the national body that oversees R&D using the UK’s world leading health data resources and expertise
  2. Learn and apply your quantitative (e.g. computational, mathematical, engineering) undergraduate or equivalent skills in a health and medical setting, even if you have had no prior experience in health or medicine or biology.
  3. Have a range of exciting research project opportunities in the final 4 months of the course, including working in the NHS, and pharmaceutical industry. Our exciting range of non-academic partners, including informatics teams from 4 Southwest NHS partners, will contribute research projects and seminars. These seminars will not be officially aligned with the modules, but some will align well with a module – such as a seminar from pharma partner using genetics in the stratified medicine module, others, will be focused more on career opportunities and be more ad hoc. We have selected them: i) to provide links to real world applications of health data; ii) to showcase the range of careers open to you; and iii) to fit to our two main themes – health services research and stratified medicine – two areas in which University of Exeter researchers excel.
  4. Interact with and be taught by leading researchers in health data, including those in Genomic and personalised Medicine, electronic medical records and operational research in the NHS.
  5. Experience a genuine interdisciplinary environment – the course will be delivered by experts in Maths, computing and medical research across two colleges.
  6. Work with some of the world’s leading health data resources, including the UK Biobank, NHS medical records, the 100,000 genomes project and the new 5 million NHS patient cohort

 

Five students per year will be able to apply for fully funded (stipend and fees) MSc studentships over the first 3 intakes (2020-2022).

 

You will be equipped to work in health and biomedical interdisciplinary teams and to tackle the exciting opportunities and challenges in health data science across a wide range of careers. You will focus on two broad areas in which Exeter excels – health services research and modelling, and stratified medicine, including genomics.

 

Your career progression will be supported by having access to seminars and visits to different environments in industry and in NHS Trusts. The University will also provide funds for attendance at HDRUK workshops, and, through our Institute of Data Science and Artificial Intelligence, ATI meetings and conferences you will be introduced to the full range of careers open to you.

3. Educational Aims of the Programme

The broad educational intentions of the programme are to provide students from strong quantitative backgrounds with the skills and experience to work as part of interdisciplinary teams using health data. The objectives are designed for those of you with existing strong quantitative skills, particularly computing programming, but who may not have any previous health or biomedical experience. You will be based in a Medical school for most of the MSc and much of what you will learn will be closely aligned with the needs of the National Health Service (NHS). For two 2 of the modules (combined 1/6th of the course) you will taught by College of Engineering, Maths, Physics and Computing staff where you will work alongside students from computational and mathematical backgrounds but who are focused on non-health related topics, such as business. For 1/6th of the course you will work alongside students from medical and health care backgrounds, such as doctors and pharmacists starting their academic careers. This diverse range of fellow students, tutors and environments, will ensure you have a thoroughly fulfilling experience.

 

Our distinctive learning intentions are:

 

  1. To provide you with the skills to apply existing programming skills to languages well known and used in health-related data, especially Python, in order to be proficient in computational handling of large complex health related datasets
  2. To enable you to understand the concepts of machine learning, artificial intelligence, and health related statistics, and if and when they should be applied to hypothesis-free and hypothesis driven questions.
  3. To provide you with a basic understanding of health statistics as applied to the needs of the NHS, such as clinical trials and modelling and prediction of health outcomes.
  4. To enable you to understand the principles, challenges, breadth and limitations of working with human data. This objective will include an understanding of the need for scalable and generalisable solutions within healthcare.
  5. To train you to apply skills in computing to two broad areas of health data research that represent important opportunities for UK heath data research, – health services research (understanding, simulating and optimising health care pathways), and stratified medicine (primarily preparing for the widespread use of genetic information).
  6. To gain communication and presentation skills in order to disseminate findings from health data science research to health care staff and researchers with no expertise in data. You will emerge with enhanced skills in critical appraisal, presenting and writing for a health care audience.
  7. Through a research project, you will apply your computing and or maths skills to working as part of an interdisciplinary team that includes people with expertise in health care or data science. These people and teams will include those outside of academia, including in the NHS, data companies such as NHS digital and industry, including pharmaceuticals.

 

The programme has been designed to be flexible and well-defined with seven integrated and core modules - four modules are 15 credits, two are 30 credits plus a 60 credit research project all at Masters level that can be studied on a part-time or full-time basis. A blended approach to learning will be achieved through distance learning, taught sessions, bespoke tutorials with guest speakers, practical exercises and projects taken at partner sites. All learning will be supported by online resources on the University of Exeter’s electronic learning environment (ELE). Expert tutors and guest lecturers will represent an appropriately diverse range of expertise from data and research backgrounds.

4. Programme Structure

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.

Our PGT programmes are designed as standalone courses but the College also aims to provide flexibility where possible. We recognise that you may sometimes wish to take a module from elsewhere in the College, or the wider University, to fit in with their specific research or professional interests.  A taught Masters degree is made up of 180 credits, usually 120 credits of taught modules and 60 credits of dissertation.  Normally at least 90 credits of taught modules (but an absolute minimum of 60 credits), plus the dissertation, will need be taken from within the named award in order to graduate with that award. That means that 30 credits could be made up from one or more appropriate modules from another Masters level programme(s) in place of your scheduled ones, depending on the module prerequisites, the contact days of the modules, and your funding source.  There may be an additional fee associated with certain postgraduate modules. It is also important to note that the timing of your new module’s contact days and assessments might conflict with your existing programme of studies.  If after considering these factors you would like to explore this option further, please discuss this with the MSc’s Programme Director.  The CMH PGT Support team can then advise about the application process, which would then go for approval from the Programme Director of your current programme and the Module Lead and Programme Director of the programme(s) in which your new module(s) sits 

 

The one year MSc Health Data Science programme is a 12 month full-time equivalent programme of study at National Qualification Framework (NQF) level 7 (as confirmed against the FHEQ). The MSc may be also taken part-time over two or three years. Programmes are 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.

 

The MSc requires 180 credits, of which 120 are taught modules and 60 is a research project.

 

Interim Awards:

 

The one-year Postgraduate Certificate (PGCert) in Health Data Science is also offered, consisting of 60 credits of taught modules – the two “fundamentals” modules in term one, and one of the themed modules in term 2.

 

The two-year part-time Postgraduate Diploma (PGDip) in Health Data Science is also offered, consisting of 120 credits of taught modules.

 

 

Summary of structure-  

Exeter’s MSc in Health Data Science will be delivered by research-active academics from two Colleges – the College of Medicine and Health (CMH) and the College of Engineering, Maths, Physics, and Computer science (CEMPS). Our non-academic partners, including the NHS and industry (including two international partners), will contribute to taught modules, and provide 50% of your research projects. This approach will result in a genuine inter-disciplinary training. The syllabus will be delivered in three broad parts, each of 60 credits. For the full time option, in Part one, Autumn term, the students will undertake four 15 credit taught modules. These modules are part of existing MSc courses in Data Science and Health research methods and will be delivered by academics from the two Colleges. In part 2, Spring term, you will undertake two 30 credit modules, one in each of our two broad themes – in Health services research (“Making a difference with health data”), and in Stratified medicine. In Part 3, summer term, you will be embedded in health or biomedical environments to undertake your research project, choosing a project from one of the two main areas – health services or stratified medicine. At least 50% of each student cohort will benefit from undertaking a project outside of academia. For the 2 and 3 year part time options, you will take fewer modules each term.

 

The syllabus focuses on two broad areas of importance to health and in which Exeter excels, providing students with a rich training environment and opportunities for real world application of their new skills.

We have chosen two broad areas on which to focus the MSc for two reasons – the very large careers market for suitable skilled data scientists in these areas of healthcare, and Exeter’s excellence in them. The health services research theme (labelled “Making a difference with data”) is important because it provides real world, demonstrable impact in improving health care delivery in the NHS. The Institute of Health Research within Exeter’s College of Medicine and health has strong links to NHS Trusts in the South West as well as nationally.  For example, you will be taught by staff from “PenCHORD”, a research team that is focussed on the application of operational research tools for health services. You will be taught from real examples implemented into the NHS, for example modelling emergency stroke care pathways, that has shown considerable impact in reshaping services, reducing wait times and improving disability outcomes (see: http://clahrc-peninsula.nihr.ac.uk/penchord).  In the Stratified medicine theme you will learn to understand if and when clinicians should intervene differently with different individuals or groups of the population. This theme will have a strong human genomics component, which is a particular strength at Exeter. From 2019, all seriously ill children and adults with rare diseases or hard-to-treat cancers will be offered whole genome sequencing as part of their NHS care. You will be able to work with some of this data which will expand on the 100,000 Genomes Project and see 1 million genomes sequenced in 5 years. Exeter hosts the South West Genomic Medicine Centre that coordinates testing for the 100,000 Genomes Project. Exeter’s excellence in human genomics is exemplified by our world leading research in common and rare genetic disease, our translation of genetic research into NHS diagnostic testing for single gene forms of diabetes, and our role as one of only seven UK centres hosting an MSc in Genomic Medicine.

 

Note that the The Postgraduate Certificate (PGCert) in Health Data Science, consisting of 60 credits of taught modules will consist of the two “fundamentals” modules in term one, and one of the themed modules in term 2.

Stage 1


Compulsory Modules

The following tables describe the programme and constituent modules. Constituent modules may be updated, deleted or replaced as a consequence of the annual review of this programme. Details of the modules currently offered may be obtained from the College website once online (late Nov, early Dec): 

http://www.exeter.ac.uk/postgraduate/taught/datascience/ 

In the first year of the MSc, all modules are compulsory, but we will provide you with some choices with the option of taking a research projects in one of the two broad themes – health services research or stratified medicine.

Full-time MSc: You will take all 180 credits of the modules listed above in one academic year.

Two-year part time MS: you will take modules marked a and one of the 30-credit modules in your first year, and the remaining 120 credits in you second year.

Three-year part-time MSc: You will take modules marked a in your first year and modules marked b in your second year. You should not take more than one 30-credit module in any one year.

PGCert (1 year): you will take modules marked a and one of the 30-credit modules to make up 60 credits required for this awards

PGDip (2-year part time): you will take modules marked a in your first year plus one 30-credit module, and modules marked b in your second year with another 30-credit module, to make the 120 credits required for this award.

CodeModule Credits Non-condonable?
HPDM139 Coding for Machine Learning and Data Science a15No
HPDM092 Fundamentals of Research Design a15No
HPDM096 Health Statistics for Data Scientists b15No
ECMM445 Learning from Data b15No
HPDM097 Making a Difference with Health Data 30No
HPDM098 Stratified Medicine 30No
HPDM099 Research Project 60No

6. Programme Outcomes Linked to Teaching, Learning and Assessment Methods

Intended Learning Outcomes
A: Specialised Subject Skills and Knowledge

Intended Learning Outcomes (ILOs)
On successfully completing this programme you will be able to:
Intended Learning Outcomes (ILOs) will be...
...accommodated and facilitated by the following learning and teaching activities (in/out of class):...and evidenced by the following assessment methods:

1. A basic understanding of machine learning and medical statistics, where they differ and how they should be used to perform hypothesis and hypothesis-free data based experiments
2. Experience of performing operational research, including using computer simulations to test “what if” scenarios in health care settings
3. Programming in Python and statistical analysis in R
4. A basic understanding of human genetic concepts and how genetic risk scores may be applied to common diseases to stratify risk

The course will be delivered in a blended fashion including: face to face contact days, web-based learning, lectures, seminars, workshops, master-classes, specialist tutorials, practical sessions, resource gathering and in-depth reading, preparation and writing of assignments.

 

Some seminars will be given by our external partners.

An assortment, detailed in the module descriptors, of course work, computer lab exercises, written assessments (1500-2500 words), Short answer tests, Oral presentations.

Intended Learning Outcomes
B: Academic Discipline Core Skills and Knowledge

Intended Learning Outcomes (ILOs)
On successfully completing this programme you will be able to:
Intended Learning Outcomes (ILOs) will be...
...accommodated and facilitated by the following learning and teaching activities (in/out of class):...and evidenced by the following assessment methods:

1. Apply analytical skills to investigate and test new hypotheses;
2. Integrate information from a variety of sources to construct a coherent thesis on a scientific topic;
3. Construct hypotheses pertinent to topical questions for health services and the use of genetics in health care
4. Evaluate the importance and relevance of data driven results in the context of previous work
5. Summarise and disseminate information including test results in oral and written forms to colleagues, patients and the public;
6. Critically evaluate the published literature with respect to the application of health services and the use of genetics in health care.

The course will be delivered in a blended fashion including: face to face contact days, web-based learning, lectures, seminars, workshops, master-classes, specialist tutorials, practical sessions, resource gathering and in-depth reading, preparation and writing of assignments.

 

Some seminars will be given by our external partners.

An assortment, detailed in the module descriptors, of course work, computer lab exercises, written assessments (1500-2500 words), Short answer tests, Oral presentations.

Intended Learning Outcomes
C: Personal/Transferable/Employment Skills and Knowledge

Intended Learning Outcomes (ILOs)
On successfully completing this programme you will be able to:
Intended Learning Outcomes (ILOs) will be...
...accommodated and facilitated by the following learning and teaching activities (in/out of class):...and evidenced by the following assessment methods:

1. Critically appraise and analyse the scientific literature on relevant subject and the ability to judge and interpret findings;
2. Engage and communicate effectively with diverse communities including the lay public and professionals involved in research and clinical practice but with little or no experience with data;
3. Exercise initiative and personal responsibility;
4. Make decisions in complex and unpredictable situations;
5. Learn independently as part of a commitment to continuing professional development.

The course will be delivered in a blended fashion including: face to face contact days, web-based learning, lectures, seminars, workshops, master-classes, specialist tutorials, practical sessions, resource gathering and in-depth reading, preparation and writing of assignments.

 

Some seminars will be given by our external partners.

Oral presentations will ensure students with strong data skills are able to communicate to non-data scientists, clinicians, other health care professionals, and the public.

 

A report from research project placement will assess professionalism as well as communication skills.

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.

 

Each module will include both formative and summative elements (please refer to the module descriptors). Formative assessment will involve opportunities for discussion and feedback from peers and tutors. The summative assessment element for each module will provide students with the opportunity to demonstrate achievement of the intended learning outcomes. Elements of assessment will include:

 

  • Operational research forecasting competition (each student will build a module based on real data from the NHS but blinded to what happened afterwards)
  • Short answer questions
  • Presentations
  • Programming tasks

 

Detailed and specific marking criteria for each assignment will be made available through the University’s electronic learning environment (ELE, http://as.exeter.ac.uk/it/systems/ele/ ) and will be clearly articulated to students throughout the modules. The overall pass mark for all modules is 50% (including the Research Projects). Where module assessment involves more than one element of coursework, a student is required to achieve a minimum of 50% in each of these elements.

 

It is compulsory for students to complete the Academic Honesty and Plagiarism module before receiving marks for assessed coursework.

 

Classification

The marking of modules and the classification of awards broadly corresponds to the following marks:

Postgraduate Degrees

Distinction   70%+

Merit            60-69%

Pass            50-59%

 

Full details of assessment regulations for UG programmes and PGT programmes assessment regulations can be found in the Teaching Quality Assurance Manual (TQA) on the University of Exeter website. Generic marking criteria are also published here.

 

Please see the Teaching and Quality Assurance Manual for further guidance.

Classification

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

Personal and Academic tutoring: It is University policy that all Colleges should have in place a

system of academic and personal tutors. The role of academic tutors is to support you on individual

modules; 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.

 

Resources: A Programme Handbook will be provided which includes information about the structure

of the programme, learning resources available, assessment methods, criteria, and regulations and

student support services. All modules will be supported by the University of Exeter’s ‘Exeter Learning

Environment' (ELE). Each module has an ELE page and discussion board for information,

announcements and resources. ELE also hosts library links, including a list of electronic journals held

by the University of Exeter. There is a page called Library and Research Skills, where you will find a

whole host of additional resources to support you in the development of your writing skills, reading

skills, generic research skills and study strategies.

Student/Staff Liaison Committee enables students & staff to jointly participate in the management

and review of the teaching and learning provision.

9. University Support for Students and Students' Learning

Please refer to the University Academic Policy and Standards guidelines regarding support for students and students' learning.

10. Admissions 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 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.

(HDRUK is a professional and statutory regulatory body (PSRB) that have provided us with funding.

13. Methods for Evaluating and Improving Quality and Standards

The University and its constituent Colleges review the quality and standard of teaching and learning in all taught programmes against a range of criteria through the procedures outlined in the Teaching Quality Assurance (TQA) Manual Quality Review Framework.

14. Awarding Institution

University of Exeter

15. Lead College / Teaching Institution

Faculty of Health and Life Sciences

16. Partner College / Institution

Partner College(s)

Not applicable to this programme

Partner Institution

Not applicable to this programme.

17. Programme Accredited / Validated by

0

18. Final Award

MSc Health Data Science (P/T 2 year)

19. UCAS Code

Not applicable to this programme.

20. NQF Level of Final Award

7 (Masters)

21. Credit

CATS credits

180

ECTS credits

90

22. QAA Subject Benchmarking Group

23. Dates

Origin Date

21.11.2019

Date of last revision

14/04/2021