Overview
- Degree awarded
- MSc/PGDip/PGCert
- Duration
- MSc 3yPT/1yFT Dip 2yPT/1yFT Cert 12-18m PT/6m FT
- Entry requirements
-
We require an honours degree (minimum Upper Second) or overseas equivalent in:
- mathematics
- statistics
- computer science
- physical science
- biomedical science (including epidemiology, biological sciences or medicine/nursing)
Your degree must have had significant statistical and computational elements and be from a recognised institution or an approved and relevant postgraduate qualification (minimum postgraduate diploma or equivalent).
We may also accept the equivalent of previous advanced study, research and/or relevant professional experience that the University accepts as qualifying the candidate for entry.
In the case of non-UK applicants, the institution certifying advanced study must be recognised and approved by the University.
You may be accepted for entry to the postgraduate diploma track with a Lower Second degree or under subject to interview, and be moved to the master's track on achieving suitable results in the taught part of the course.
- How to apply
Please apply via our online application form , following any instructions for completion carefully. See the Application and selection section for details of the supporting documents that we require. We recommend that you apply as early as possible. We reserve the right to close applications if the course is full.
Please note: Due to a high volume of applications this course is now closed to international applicants. Home students wishing to submit an application should follow the link to the online application form.
Course options
| Full-time | Part-time | Full-time distance learning | Part-time distance learning | |
|---|---|---|---|---|
| MSc | Y | Y | N | N |
| PGDip | Y | Y | N | N |
| PGCert | Y | Y | N | N |
Course overview
- Learn how to work within a team to collect and interpret health data and use it to help solve healthcare delivery challenges.
- Experience key technical skills and software for working with and manipulating health data.
- Have the option to apply data science skills to imaging and multi-omics data to understand disease, put data into practice in clinical decision support systems and digital transformation, and study advanced statistical and machine learning methods.
- The course is delivered through a mix of face to face teaching and online learning, designed to give you opportunity to apply it to real-world case-studies. Assignments are relevant to the types of work that health data scientists contribute to and lead, and many students complete a dissertation on a large programme research grant or with external organisations.
- Access sophisticated research and teaching facilities at the Centre for Health Informatics (CHI).
- Open up a wealth of career opportunities in the NHS, industry and academia - an estimated 52,000 data scientists are needed in the UK alone.
Open days
Fees
For entry in the academic year beginning September 2023, the tuition fees are as follows:
-
MSc (full-time)
UK students (per annum): £11,500
International, including EU, students (per annum): £28,500 -
MSc (part-time)
UK students (per annum): Year 1 £4,600; Year 2 £4,600; Year 3 £2,300
International, including EU, students (per annum): year 1 £11,400, year 2 £11,400 and year 3 £5,700 -
PGDip (full-time)
UK students (per annum): £9,200
International, including EU, students (per annum): £22,800 -
PGDip (part-time)
UK students (per annum): £4,600
International, including EU, students (per annum): 11400 -
PGCert (full-time)
UK students (per annum): £4,600
International, including EU, students (per annum): £11,400 -
PGCert (part-time)
UK students (per annum): £2,300
International, including EU, students (per annum): £5,700
Further information for EU students can be found on our dedicated EU page.
The course fees include all the tuition, technical support and examinations required for the course. All fees for entry will be subject to yearly review. Courses lasting more than one year may be subject to incremental rises per annum. For general fees information please visit: postgraduate fees . Always contact the department if you are unsure which fee applies to your qualification award and method of attendance.
Additional expenses
The University permits applicants with comparable previous experience to submit an application for consideration of AP(E)L Accreditation Prior (Experiential) Learning. The maximum AP(E)L is 15 credits to a PGCert, 45 credits to a PGDip and 60 credits to a master's.
If your AP(E)L application is successful, the University charges £30 for every 15 credits of AP(E)L. The overall tuition fee is adjusted and then the administrative charge is applied.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
Contact details
- School/Faculty
- Faculty of Biology, Medicine and Health
- Contact name
- Postgraduate Admissions Team
- Telephone
- +44 (0)161 529 4563
- pgtaught.cbm@manchester.ac.uk
- Website
- https://www.bmh.manchester.ac.uk/medicine/
- School/Faculty
-
Faculty of Biology, Medicine and Health
Courses in related subject areas
Use the links below to view lists of courses in related subject areas.
Entry requirements
Academic entry qualification overview
We require an honours degree (minimum Upper Second) or overseas equivalent in:
- mathematics
- statistics
- computer science
- physical science
- biomedical science (including epidemiology, biological sciences or medicine/nursing)
Your degree must have had significant statistical and computational elements and be from a recognised institution or an approved and relevant postgraduate qualification (minimum postgraduate diploma or equivalent).
We may also accept the equivalent of previous advanced study, research and/or relevant professional experience that the University accepts as qualifying the candidate for entry.
In the case of non-UK applicants, the institution certifying advanced study must be recognised and approved by the University.
You may be accepted for entry to the postgraduate diploma track with a Lower Second degree or under subject to interview, and be moved to the master's track on achieving suitable results in the taught part of the course.
English language
International students must demonstrate English proficiency through a secure and approved testing system. We ask for English language proof from applicants from non-majority English speaking countries (a list of majority English-speaking countries, as defined by the UK Home Office, can be found here ).
Specifically, we require a minimum of:
- IELTS: Overall 6.5 (6.0 in reading and writing)
- TOEFL iBT: 90 (25 in reading and writing)
See further information about requirements for your country .
If you envisage any difficulties in satisfying our English language requirements then please do let us know. The University offer a number of pre-sessional English courses designed specifically to help international students meet our requirements prior to the start of their course.
English language test validity
Other international entry requirements
Self funded international applicants for this course will be required to pay a deposit of £1,000 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. This deposit will only be refunded if immigration permission is refused. We will notify you about how and when to make this payment.
Please upload a copy of your current valid passport with your application showing the photograph page with your application. For CAS purposes, this must show your full name, date of birth, nationality, passport number and the date the passport is valid until, which must be later than the date of your planned arrival in the UK, and the start date of your course. You cannot use your CAS to apply for a visa more than three months before the start date of your course.
Your CAS number is only valid for one Tier 4 application.
Application and selection
How to apply
Please apply via our online application form , following any instructions for completion carefully. See the Application and selection section for details of the supporting documents that we require. We recommend that you apply as early as possible. We reserve the right to close applications if the course is full.
Please note: Due to a high volume of applications this course is now closed to international applicants. Home students wishing to submit an application should follow the link to the online application form.
Advice to applicants
Your application form must include the following documents:
- two academic/professional references on letter headed paper, signed and dated within six months of your application. Unfortunately we cannot request references from your referees on your behalf;
- official academic certificates and transcripts of all your current and previous studies (with fully certified translations by a professional translator if not in English);
- personal statement of approximately 300-500 words about why you wish to take this course and how it will affect your personal and professional development;
- CV including details of relevant professional/voluntary experience;
- a scanned copy of the photograph page of your passport;
- proof of English Language proficiency (if appropriate).
Supporting documents can be uploaded and submitted with the online application form, or sent directly to pgtaught.cbm@manchester.ac.uk .
Your application cannot be considered until all your supporting documents are supplied. Please include your University ID in all correspondence.
How your application is considered
Interview requirements
Overseas (non-UK) applicants
Re-applications
If you applied in the previous year and your application was not successful, you may apply again.
Your application will be considered against the standard course entry criteria for that year of entry. In your new application you should demonstrate how your application has improved.
We may draw upon all information from your previous applications or any previous registrations at the University as a student when assessing your suitability for your chosen course.
Course details
Course description
Our MSc Health Data Science course aims to create a new breed of scientist who can understand the healthcare sector and medicine, how data is collected and analysed, and how this can be communicated to influence various stakeholders.
The current model of healthcare delivery in the UK is subject to unprecedented challenges. An ageing population, the impact of lifestyle factors and increasing costs mean that the existing approaches may become unsustainable.
This, coupled with a drive towards personalised medicine, presents an opportunity for a step change in healthcare delivery.
To do this, we need to make best use of the health data we collect and create a better understanding of the relationship between treatments, outcomes and patients.
This MSc promotes the need for translational thinking to provide the knowledge, skills and understanding that will be applied across new challenges within healthcare delivery.
A multidisciplinary approach to health data science is the focus of this course, including students from a variety of professional backgrounds. The structure of the MSc ensures that you will share knowledge with each other and learn to work in multidisciplinary teams, rather than in specialist silos.
You will be taught by world-leading professionals and academics in the field of health data science, statistics, machine learning, information engineering, omics and digital biology, and digital transformation of the healthcare system.You will also mix with students from a range of disciplines from all over the world.
The current structure of the course (subject to change) involves four mandatory units in the first semester including:
- Introduction to Health Data Science
- Modern Information Engineering
- Fundamentals of Statistics and Mathematics
- Statistical Modelling and Inference in Health.
The second semester offers nine optional units, of which four need to be completed (for those studying for a MSc).
The optional units include:
- Statistics for Randomised Trials
- Tutorials in Advanced Statistics
- Principles of Digital Biology
- Healthcare Multi-Omics
- Machine Learning and Advanced Data Methods
- Computational Imaging (including machine learning techniques)
- Clinical Decision Support
- Digital Transformation
- Introduction to Health Informatics
PhD with integrated master's
If you're planning to undertake a PhD after your master's, our Integrated PhD programme will enable you to combine your postgraduate taught course with a related PhD project in biology, medicine or health.
You can also visit this page for examples of projects related to integrated master's courses.Aims
This course will allow you to:
- gain key background knowledge and an understanding of the healthcare system, from the treatment of individuals to the wider population;
- gain an understanding of the governance structures and frameworks used when working with health data and in the healthcare sector;
- experience key technical skills and software for working with and manipulating health data;
- understand the breadth and depth of application methods and the potential uses of health data;
- comprehend key concepts and distinctions of the disciplines that need to be synthesised for effective health data science;
- appreciate the role of the health data scientist and how they fit into the wider healthcare landscape;
- understand the importance of patient-focused delivery and outcomes;
- develop the in-depth knowledge, understanding and analytical skills needed to work with health data effectively to improve healthcare delivery;
- develop a systematic and critical understanding of relevant knowledge, theoretical frameworks and analytical skills to demonstrate a critical understanding of the challenges and issues arising from heterogeneous data at volume and scale, and turn them into insight for healthcare delivery, research and innovation;
- apply practical understanding and skills to problems in healthcare;
- work in a multi-disciplinary community and communicate specialist knowledge of how to use health data to a diverse community;
- evaluate the effectiveness of techniques and methods in relation to health challenges and the issues addressed;
- extend your knowledge, understanding and ability to contribute to the advancement of healthcare delivery knowledge, research or practice through the systematic, in-depth exploration of a specific area of practice and/or research.
Special features
Research project options
MSc students will have an opportunity to conduct their research project in other settings such as the NHS and the biopharmaceutical industry, as well as academia.
Teaching and learning
The course covers four main areas that bring together technical, modelling and contextual skills to apply these to real world problems when harnessing the potential of health data.
In each of the units that deliver the key skills, both the importance of the patient and the governance surrounding working in the healthcare environment (especially structures around information governance) is embedded throughout.
Each unit will use case studies provided by existing work and research at the CHI . The course will focus on large and complex health datasets (often routinely collected) in environments that safeguard patient confidentiality.
The course will encourage intellectual curiosity, creativity, and critical thinking, providing transferable skills for lifelong learning and research and cultivation of reflective practice.
Through the development of these innovation, critical, evaluative, analytical, technical, problem solving and professional skills, you will be able to conduct impactful work and advance healthcare delivery.
We see learning and teaching as collaborative knowledge construction, which recognises the contribution of all stakeholders (academic staff, service users and carers and students). This is demonstrated in the course through contributions made by these stakeholders through case studies, examples, invited seminars and participation in group work.
A variety of teaching methods will be used within the constraints of the method of delivery. The course will be student centred and will be delivered from the outset using a combination of face-to-face, distance learning and blended learning units.
Coursework and assessment
A range of assessments are used within each course unit and across the course as a whole.
All assessments require you to integrate knowledge and understanding, and to apply this to case studies and the outcomes of each unit.
Assessment will occur in a variety of forms including (but not exclusively) essays, case studies, assessed seminar/tutorial presentations and literature reviews.
Written assignments and presentations have a formative role in providing feedback (particularly in the early stages of course units) as well as contributing to summative assessment.
Online quizzes provide a useful method of regular testing, ensuring that you actively engage with the taught material.
The assessment of tutorials contains an element of self and peer evaluation, so you can learn the skills associated with the effective management of and participation in collaborative activity.
The course also places an emphasis on group work, as this a vital skill for professionals operating in a multidisciplinary area such as health data science, and this is shown in the teaching methods and assignments.
Each unit has a different emphasis on the group work assessment based on the nature of the material being covered, how they are to apply the knowledge and the work they are to complete.
The dissertation for the MSc requires you to undertake an extended written piece of work (10,000 to 15,000 words) that focuses on a specific aspect of health data science.
Course unit list
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
| Title | Code | Credit rating | Mandatory/optional |
|---|---|---|---|
| Principles of Digital Biology | COMP60532 | 15 | Mandatory |
| Introduction to Health Informatics | COMP60542 | 15 | Mandatory |
| Modern Information Engineering | IIDS61311 | 15 | Mandatory |
| Decision Support Systems | IIDS61402 | 15 | Mandatory |
| Mathematical Computing for Medical Imaging | IIDS67462 | 15 | Mandatory |
| Tutorials in Advanced Statistics | IIDS67612 | 15 | Mandatory |
| Fundamental Mathematics & Statistics for Health Data | IIDS67631 | 15 | Mandatory |
| Statistical Modelling and Inference for Health | IIDS67641 | 15 | Mandatory |
| Introduction to Health Data Science | IIDS67681 | 15 | Mandatory |
| Machine Learning and Advanced Data Methods | IIDS67682 | 15 | Mandatory |
| Displaying 10 of 18 course units | |||
| Display all course units | |||
Course collaborators
Our course is embedded in the rich ecosystem of world-class research in the field across the University, and is at the forefront of driving the professionalisation and skills agenda.
The MSc is hosted by the Centre of Health Informatics (CHI), which has built an internationally recognised multi- and trans-disciplinary research base attracting over £50 million of funding from research councils, the EU and industry.
CHI is part of the Health Data Research UK Northern Partnership and is leading research in the Learning Healthcare System in which data analysis is used to directly improve health care (including improvements in care for elderly and antibiotic prescribing).It conducts world-leading research in health informatics and forms a centre of excellence in digital health innovation for North England. The centre has a national role in driving advanced methodological research to harness health data; and to build capacity in health informatics and data analytics.
CHI is also conducting key research projects such ClinTouch, a mobile early warning system. CHI has experience of delivering research-led innovative education that remains relevant to the needs of the market.
This centre runs a programme of research and education that brings experts together from a wide range of academic, NHS and industrial partners.
Facilities
You will have access to CHI's state-of-the-art research and teaching facilities.
In addition, you will have full access to the University's IT and library facilities . This will include Blackboard and other e-learning facilities.
Each student will have an identified personal tutor who can provide advice and assistance throughout the course. During the research project, you will be in regular contact with your research supervisor.
Disability support
Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service .
Careers
Career opportunities
Data science is increasing in importance in the fields of medicine and public health, where we can determine how to best design treatments and allocate resources based on analysis of health and related data.
For students completing this MSc, there will be a wealth of career opportunities available in the NHS, industry and academia. It is envisaged there is a need for 52,000 data scientists in the UK alone.
Students completing the master's course will be eligible for consideration for PhD programmes within the Faculty of Biology, Medicine and Health, and you will be encouraged and supported to develop relevant research proposals.
Many students have gone on to study for a PhD or gone into the healthcare or commercial sectors.