Health Data Science (April 2024)
Data science is revolutionising the way we understand healthcare delivery and impact. This course, offered by the Usher Institute, will equip healthcare professionals, and other interested learners, with the key foundations and data skills that are needed to maximise opportunities in this domain. It is acknowledged there is a shortage of data skills in the healthcare sector and this is the perfect course for someone looking to gain core knowledge and capabilities.
The course provides an introduction to key concepts, principles and methods of data science in health, enabling exploration of the potential for data to transform healthcare. You will learn the basics of R programming, and how to use current data science tools to process healthcare data. You will also about effective analysis and reporting using R programming language and gain practical experience in working with data. Additionally, you will gain critical understanding of ethical and legal implications of working with healthcare data.
Further information can be found in the University's course catalogue:
This course is accredited by the Federation for Informatics Professionals in Health and Social Care (FEDIP). FEDIP accreditation highlights educational providers who are furthering the development of the health and care informatics profession. The Federation is comprised of the leading professional bodies in health and care informatics and accredits this course as part of our recommended learning for health and care informatics professionals in this field.
This course is for anyone working in health or social care or computational roles who is interested in starting a career in data science OR graduates who are seeking to develop data science skills that can be applied in health, social and care services.
This is an introductory Masters-level course (SCQF Level 11). It provides foundational skills and/or an overview of the subject – no prior knowledge is needed. Masters-level courses are relatively intensive and require independent learning, critical thinking, analysis and reflection.
As this course is designed mainly for health and care professionals, we expect our students to have qualifications or work experience in such environments (e.g. NHS National Services, Acute/Community/Public health), Third sector organisations, Social Services, Nursing homes, pharmaceutical companies, diagnostic laboratories, etc.). An understanding of the fundamentals of maths and statistics (at Higher level) would be advantageous but is not a prerequisite for joining the course.
You should be educated to a degree level as this course is catering for those seeking postgraduate academic credit. However, professionals who are involved with managing services and caseloads and have 5 years of work experience may also apply even if they do not hold a degree qualification.
Check whether your international qualifications meet our general entry requirements:
Before you apply for this course, you will need to complete a Byte Size Learning course (Introduction to Health Data Analytics and R), which will provide an introduction to Health Data Science.
Our Byte Size Learning is hosted on the Federation for Informatics Professionals in Health and Social Care (FEDIP) Hub. Introduction to Health Data Analytics and R contains approximately an hour of content, and you will be able to apply for a CPD certificate upon completion.
To complete your Byte Size Learning, you will need to request access to the FEDIP Hub. Anyone can access the Hub for a 6-month trial for free, after which you must become FEDIP registered in order to maintain access. Request your free 6-month trial to the FEDIP Hub https://bit.ly/create_account_hub
You will be required to upload proof that you have completed this Byte Size course at the point of application for Health Data Science.
You must be comfortable studying and learning in English if it is not your first language.
- Explain and critically discuss key concepts, principles and methods of data science in health.
- Apply a range of specialised data science techniques to different medical and healthcare scenarios.
- Analyse health data with the use of the R programming language, including summarisation, visualisation and interpretation.
- Critically examine the ethical, societal and regulatory principles and implications of data science in health.
This is a 10-week online course (plus 2 weeks for assessment), comprising a total of 100 hours of study.
It is based around short recorded videos, which are complemented with readings and discussions in the forums. Hands-on programming tasks in R will equip students with key data skills, and online tutorials will allow students to ask questions and discuss topics of interest.
Health Data Science is designed for busy health professionals and all live lectures will be recorded so students can view them at a convenient time.
There are two assignments which need to be completed and some live tutorials, usually scheduled at 10am and 6pm. These will also be recorded, but it is recommended you try to attend at least one of the time slots. The course will require engagement throughout.
Usually, live tutorials are scheduled for Tuesday and Wednesdays at 18:00, and there is a live tutorial repeat session on Thursday at 11:00 in weeks 2, 4, 6, and 8. The course organiser also offers regular online drop-in sessions: Thursdays at 6pm. All tutorials and drop-in sessions are recorded and available afterwards.
There are no exams for this course. The students' final mark will be based on the following:
- Weekly Quizzes with a short number of questions on the topics covered each week (20% i.e. 10 weeks x 2%)
- Programming Assignment where you use R to process, analyse and visualise data, as well as report on your analysis (50%)
- Essay-Style Assignment where you discuss the societal and ethical implications of data science in health (30%) 1,000 words
Assessment is 100% coursework
Course fees for 23/24 are £1065 but funded places may be available for people employed or unemployed in Scotland (residency requirements apply).
Funding
Through the Scottish Funding Council (SFC) Upskilling Fund, a limited number of fully-funded places are available on Data Upskilling Short Courses at The University of Edinburgh.
Eligibility
Funded places are available to those who meet SFC fee waiver criteria:
“Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.”
If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria:
- be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and
- have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and
- be ordinarily resident in Scotland at the course start date.
You can find out more about residency criteria on the SAAS website or in this summary
Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. You can check your likely fee status here. Please email us at upskilling@ed.ac.uk if you would like to discuss your funding eligibility before applying.
Please note that full-time students (including full-time PhD students) are not eligible for funding.
You will receive a certificate of completion after the final assessment date if you have submitted your coursework.
Applications for April 2024 are now open.
Before you apply for this course, you will need to complete a Byte Size Learning course (Introduction to Health Data Analytics with R), which will provide an introduction to Health Data Science.
Our Byte Size Learning is hosted on the Federation for Informatics Professionals in Health and Social Care (FEDIP) Hub. Introduction to Health Data Analytics and R contains approximately an hour of content, and you will be able to apply for a CPD certificate upon completion.
To complete your Byte Size Learning, you will need to request access to the FEDIP Hub. Anyone can access the Hub for a 6-month trial for free, after which you must become FEDIP registered in order to maintain access. Request your free 6-month trial to the FEDIP Hub
You will be required to upload proof that you have completed this course at the point of application for Health Data Science.
In order to verify that you meet the entry requirements for this course, you will be required to provide either:
- a degree certificate/transcript for your highest/most relevant academic qualification
- a CV and reference from an employer (if employed) or professional associate (if unemployed) outlining your suitability for the course
The degree certificate/transcript or CV should be emailed to upskilling@ed.ac.uk within 24 hours of submitting your application. References should be emailed to the same address (from an institutional/company account if from your employer) within 1 week of submitting your application.
Once complete, your application will be processed in 1-2 weeks. Applications will be processed on a first come, first served basis with priority given to applicants who meet the criteria for a funded place. We aim to email all applicants within 2 weeks of submission regardless of the outcome of their application.
Applications will close on 1st April 2024.
This course is offered as part of Data Science for Health and Social Care , an innovative online programme looking to equip students with the data science skills, capabilities and competencies to realise the value of data in a healthcare setting. You can use credits achieved on this course towards postgraduate study on this programme (MSc, PG Diploma or PG Certificate), subject to approval by the Programme Director.
You may also be able to use credits achieved on this course towards other University of Edinburgh postgraduate programmes, subject to the approval of the relevant Programme Director.
