Data Science for Health and Social Care (Online Learning) MSc, PgCert, PgDip, PgProfDev

Level: Postgraduate taught

Subject: Data Science

Year of entry: 2027

Study options

Mode of study

Online learning

Part-time intermittent study
  • PgProfDev | up to 2 years | Start date: multiple dates
Part-time
  • PgCert | 1 year | Start date: September 2027
  • PgDip | 2 years | Start date: September 2027
  • MSc | 3 years | Start date: September 2027

Key facts

School

Edinburgh Medical School

College

College of Medicine and Veterinary Medicine

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Open Days on Demand

Dive into an extensive selection of programme-specific session recordings hosted by our Academic and Professional Services staff.


About the degree programme

Make a difference and boost your career with the MSc Data Science for Health and Social Care, where you will learn how to use data to improve lives, strengthen systems, and shape policy in the health and social care sectors. 

Whether you are starting out, changing direction, or advancing your current role, we will help you build the skills you need to deliver powerful, data-driven solutions that create lasting impact.

This is a part-time programme taught over three years and is fully online.

Through a powerful blend of theory and hands-on practice, this programme gives you the tools to leverage data for smarter decision-making and design evidence-based solutions that make a real impact.

You will build a strong foundation in the core principles of data science while developing the practical skills to interpret, analyse, and interrogate complex datasets. Learning on the programme is shaped around the needs of modern health and social care, preparing you to turn data into meaningful insights that can improve services, systems, and health and social care outcomes.

Study options

You can study for the full MSc, or leave the programme earlier with a lower award:

  • Year 1: Postgraduate Certificate (PgCert), 60 credits
  • Year 2: Postgraduate Diploma (PgDip), 120 credits
  • Year 3: Master of Science (MSc), 180 credits

Each stage builds on the one before, so your credits carry forward as you progress.

Alternatively, you can study standalone courses through Postgraduate Professional Development (PgProfDev). This route is aimed at working professionals who want to advance their knowledge one course at a time, without the time or financial commitment of a full degree.

Programme rankings

  • 20th in the world for Medical and Health (Times Higher Education World University Rankings by Subject 2026)
  • 30th in the world for Data Science and Artificial Intelligence (QS World University Rankings by Subject 2026)

Programme benefits

  • Study part-time and fully online, from anywhere and around your existing work and life. Learn to unlock the value of data to improve health, wellbeing, and care systems locally and globally on any device, anytime, without putting your life or career on hold.
  • Shape your own learning pathway with a wide range of courses covering both technical data science skills and topics in health and social care, selecting options that match your goals and customising your experience to fit your career ambitions.
  • Join a vibrant international network of professionals and students tackling health and social care challenges from diverse perspectives and build lasting connections across disciplines and borders.
  • Develop in-demand data science skills tailored specifically to the health and social care sectors. You will learn how to analyse, interpret, and apply data to drive meaningful decisions, improving outcomes, enhancing systems, and informing policy in these vital fields.
  • Open doors across health, social care and beyond, whether you are advancing your current role, changing direction or entering data science for the first time.

Usually, when undertaking courses, you learn methods, tools, techniques, etc. and understand them within the course, but don't always apply them to real-life scenarios. This is the first course I have ever undertaken which uses Health and Social Care data. This really helps me understand how to apply what I've learned to my work. Without sounding too cheesy, I am excited to use what I have learned and I really look forward to my classes (and that's a first for me). Feeling good about what you are learning and being able to apply what you have learned in some way lessens the burden of undertaking a master's when working full-time.

Paula MSc Data Science for Health and Social Care, 2026 graduate

Tuition fees

Tuition fees by award and duration

Tuition fees for full-time and part-time options are listed for one academic year.

Part-time intermittent study
Part-time

Graduate discount

If you are a University of Edinburgh graduate, you will be eligible for a 10% discount on your tuition fees for this programme. You may also be eligible if you were a visiting undergraduate student.

Find out how to receive your graduate discount

Deposit

You do not have to pay a deposit to secure your place on this programme.

Funding opportunities

Funding for postgraduate study is different to undergraduate study, and many students need to combine funding sources to pay for their studies.

Most students use a combination of the following funding to pay their tuition fees and living costs:

  • borrowing money
    • taking out a loan
    • family support
  • personal savings
  • income from work
  • employer sponsorship
  • scholarships

Explore sources of funding for postgraduate study

Online Learning Scholarships

The University offers several scholarships specifically for online, part-time postgraduate programmes. These are very competitive. We publish data on the success rate for scholarship applications to help you understand the level of demand.

Scholarships will be awarded to outstanding candidates, and will be applied toward tuition fees.

Learn more about Online Learning Scholarships

The Glenmore Postgraduate Scholarship

Four scholarships are available for eligible postgraduate programmes offered by the University. The scholarships will cover full tuition fees for the duration of the programme of study.

Glenmore Postgraduate Scholarship

Search for other funding opportunities

You can find scholarships, bursaries and other funding you might be eligible for on our Scholarships and Student Funding site.

Postgraduate funding opportunities

These entry requirements are for the 2027-28 academic year and requirements for future academic years may differ. Entry requirements for the 2028-29 academic year will be published in early Oct 2027.

Qualifications

A UK 2:1 honours degree, or its international equivalent, in a health-related degree or in a relevant subject appropriate to the programme of study such as life sciences, social sciences, mathematics, statistics, engineering, computer science or other related discipline.

We may also consider your application if you have a minimum of 3 years' relevant work experience; please contact the programme team to check before you apply. You may be admitted to certificate level only in the first instance.

International equivalent qualifications

To find international equivalent qualifications, select where you studied from the country or region list.

English language requirements

You must prove that your English language abilities are at a high enough level to study this degree programme.

This is the case for all applicants, including UK nationals.

You can meet our English language requirements with one of the following:

  • an English language test (restrictions apply)
  • a degree that was taught and assessed in English (restrictions apply)
  • certain professional qualifications

English language tests we accept

We accept any of the following English language tests, at the specified grade or higher:

  • IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
  • TOEFL-iBT (including Home Edition) before 21 January 2026: total 92 with 23 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
  • TOEFL-iBT (including Home Edition) from 21 January 2026: total 4.5 with at least 4.5 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
  • C1 Advanced (CAE) / C2 Proficiency (CPE): total 176 with at least 169 in each component.
  • Oxford ELLT: total 7 with at least 6 in each component. We do not accept Oxford ELLT Skill Retake to meet our English language requirements.
  • Oxford Test of English Advanced: total 145 with at least 135 in each component.
How old your English language tests can be
Tests no more than two years old

The following English language tests must be no more than two years old on the 1st of the month in which your programme starts, regardless of your nationality:

  • IELTS Academic
  • TOEFL-iBT (including Home Edition)
  • Oxford ELLT
  • Oxford Test of English Advanced
Tests no more than three and a half years old

All other English language tests must be no more than three and a half years old on the 1st of the month in which your programme starts, regardless of your nationality.

Degrees taught and assessed in English

We accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English-speaking country, as defined by UK Visas and Immigration.

UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English-speaking countries (non-MESC).

Approved universities in non-MESC

How old your degree can be

If you are not a national of a majority English-speaking country, then your degree must be no more than five years old on the 1st of the month in which your programme starts.

This time limit does not apply to your degree if you are a national of a majority English-speaking country.

Find out more about our English language requirements

Find out about other English language qualifications we accept, including professional qualifications.

English language requirements

What you will study

The Data Science for Health and Social Care programme is structured to give you a comprehensive understanding of how data science is applied in health and social care settings.

Year 1 (PgCert)

You will build the foundations of data science, including core concepts and R programming, and begin applying them in a health and social care context. Your option courses let you broaden into areas such as data ethics, statistics and data visualisation.

Year 2 (PgDip)

You will move on to the methods and frameworks used to design data science projects, developing your ability to plan and carry out applied research. Your options let you specialise further, for example in machine learning or natural language processing.

Year 3 (MSc)

You will bring your learning together in an independent MSc dissertation, applying your skills to a real-world research project with support from experienced supervisors. You can follow different project pathways, including a work-based project with an industry or public sector organisation, or opt to take more taught courses in your dissertation year via the SLICC (Student-Led Individually Created Course) route.

Postgraduate Professional Development (PgProfDev)

You may take a maximum of 50 credits worth of courses over two years through our PgProfDev scheme. These lead to a University of Edinburgh postgraduate award of academic credit but not an exit qualification.

Any time spent studying PgProfDev will be deducted from the amount of time you will have left to complete a Masters, Postgraduate Diploma, or Postgraduate Certificate programme.

If you wish to continue your studies with us, you can request a transfer from PPD onto a named exit award.  If your request is successful and you are eligible to transfer, you will be required to complete all compulsory courses required for your chosen exit award. You should consult your programme team before submitting a request.

Although PgProfDev courses have various start dates throughout a year, you may only start a Masters, Postgraduate Diploma, or Postgraduate Certificate programme in the month of September.

On part-time programmes, you need to study sequentially, following the standard academic timetable of your chosen programme.

Please contact the programme team for more information about available courses and course start dates.

Compulsory courses

All students take the following compulsory courses, which build the core data science and research skills the programme is based on.

Year 1
  • Introduction to Data Science in Health and Social Care (20 credits): You will gain a critical understanding of the key concepts, methods and tools of data science and how they are used to improve health, wellbeing and the management of care systems. You will build your data science toolkit with R programming, gaining hands-on experience working with real health and social care data.
Year 2
  • Foundations of Research Design in Data Science for Health and Social Care (10 credits): You will learn the principles of research design, ethics and integrity, and how to critically appraise published research. You will also develop the academic writing skills you need, laying the groundwork for your dissertation.

The following Year 2 course is compulsory for students who wish to complete the full MSc:

  • Applied Research Design in Data Science for Health and Social Care (10 credits): You will build on those foundations to plan a data science research project; identifying a research question, choosing a study design to answer it, and working to the principles of open science, good data management and reproducible research. This prepares you directly for your Year 3 dissertation, regardless of the dissertation route that you decide to take. Projects developed in the course are not necessarily the projects you will undertake in your dissertation year.

Option courses

In both Year 1 and Year 2 you choose four option courses, worth 10 credits each, so you can shape the programme around your own interests and career goals. The options range from core technical skills to applied health and social care topics, and group loosely into areas such as:

  • Programming and software: 
    • Data Types and Structures in Python and R
    • Intermediate R Programming for Data Science
    • Foundations of Software Development in Health and Social Care
    • Applied Software Development in Health and Social Care
    • Introduction to Databases and Information Systems
  • Statistics and modelling: 
    • Introduction to Statistics in Health and Social Care
    • Statistical Modelling for Epidemiology
    • Foundations of Nutritional Epidemiology
  • Machine learning and AI: 
    • Foundations of Machine Learning for Health and Social Care
    • Natural Language Processing (NLP) in Health and Social Care
    • AI for Care in the Digital Age
  • Ethics, visualisation and communication: 
    • Data Ethics in Health and Social Care
    • Data Visualisation: Knowledge Transfer
    • Introduction to GIS and Spatial Data Analysis
  • Research methods: 
    • Implementation Science: Putting Evidence-Based Interventions into Practice
    • Systematic Reviews
    • Qualitative Interviewing and Data Analysis in Health Research
  • Wider health, leadership and innovation: 
    • Leadership and Management in Public Health
    • Understanding Planetary Health and Data
    • Innovation-Driven Entrepreneurship: Data Science, Technology and Innovation

The options available differ between Year 1 and Year 2, and the list can change from year to year, so check 'Find courses for this programme' for details of the current courses and how each is taught and assessed.

Find courses for this programme

Find out what courses you can study on this programme and how each of them are taught and assessed.

The courses on offer may change from year to year, but the course information will give you an idea of what to expect on this programme.

Part-time intermittent study
Part-time

We link to the latest information available. This may be for a previous academic year and should be considered indicative.

Disclaimer

The delivery of option courses in Years 1 and 2 is subject to demand and availability.

Teaching and assessment

Teaching

This programme is taught entirely online using the flipped classroom approach. This means that students complete materials for the incoming week in their own time (videos, exercises, discussions) and then meet to practice their new skills during 1-2 hour weekly practical tutorials. This flips the typical narrative from 'first lecture together then homework alone', to create an environment for social and supported learning.

We use innovative techniques such as pair-programming. During practical tutorials students work in pairs, one taking a role of a driver (person who types and shares their screen) and the other being a navigator (person who helps and asks questions). Students change roles every 30 minutes to practice collaborative code creation and cross-pollinate their understanding.

Throughout the programme we use project-based learning meaning course content and assessments are typically based around exploring and analysing a dataset, and presenting the findings in a short publishable-quality portfolio piece. 

Real-life health and social care datasets are used both for teaching and learning as well as assessment. For courses we primarily use open datasets about various aspects of health and social care, sometimes local to Scotland or the UK where necessary context is provided.

Technical training

You will have an opportunity to learn computer programming in R, Python and SQL with a number of tools and infrastructure components (RStudio, VSCode-like environments, and so on). Your dissertation can involve interacting with secure data storage ecosystems and databases.

Assessment

Depending on the course, the assessments will be a combination of programming assignments, critical writing and data-analytics projects.

Learning outcomes

On successful completion of the MSc Data Science for Health and Social Care programme, you should be able to:

  • Demonstrate a critical understanding of the principles and methods, governance and implications of data science in health and social care.
  • Create, identify and evaluate data-driven solutions to solve complex problems in healthcare and social services contexts.
  • Demonstrate the skills and capacity to analyse facts and situations and apply creative and inventive thinking to develop appropriate solutions.
  • Articulate and effectively explain information and adapt communication styles for different people, situations and audiences.
  • Work collaboratively in a multi-disciplinary environment to deliver data-driven solutions.

Support for your studies

You will have access to a range of support services if you need them throughout your degree.

We will assign you to a student adviser, and this should be the first person to contact if you need help. They can guide you to other University service teams depending on what support you need.  

How we support you

Our academic staff

Our academic staff are at the forefront of research across data science and programming education, combining technical expertise with pedagogical innovation. 

In addition to conducting internationally recognised research in areas such as machine learning, health data science and artificial intelligence, members of our team investigate how programming can be taught more effectively through:

  • evidence-based curriculum design
  • active learning
  • authentic assessment
  • inclusive teaching practices.

This close integration of educational research with disciplinary expertise ensures our students experience teaching that is both academically rigorous and informed by the latest advances in higher education.

Dr Brittany Blankinship

Dr Brittany Blankinship is the Programme Director of the MSc Data Science for Health and Social Care and Postgraduate Quality Enhancement and Assurance Lead for the Edinburgh Medical School. 

Her work spans cognitive ageing, multilingualism, and data science for health and social care, and she is widely recognised for her innovative, learner-centred approach to teaching. Brittany is passionate about helping students feel confident with data, coding, and statistics, and is an advocate for public engagement in science. She is the lead editor of the Teaching Programming Across Disciplines book.

Teaching Programming Across Disciplines

Dr Pawel Orzechowski

Dr Pawel Orzechowski has a PhD in Computer Science and a decade of experience in programming industry and start-ups. His teaching developed in coding bootcamps where he helped career-changers to pivot into coding. 

His university teaching first in the Business School and now at Edinburgh Medical School combines his extensive technical skills with experience of working with external organisations. 

Outside of coding his expertise in UX (user experience) and system design means he will teach you using and building computers and data beyond just writing code. He is a co-organiser of the NHR-OA (OpenAnalytics) conference.

Read more about our team

How online learning works

This degree programme is taught entirely online. There is no need to come to the city or University campus. 

All learning and teaching takes place within our virtual learning environment (VLE). Through the VLE, you can: 

  • access all your learning materials and study resources, including e-books and library resources
  • interact with your tutors and classmates 

An online degree from the University of Edinburgh is academically equivalent to an on-campus postgraduate degree and involves the same level of work overall. The qualification you get is of equal value. Your degree certificate will not mention that you studied the programme online.

Watch our video to see how online learning works.

Time commitment

Our courses are delivered using a flipped classroom approach, with students engaging with bespoke readings, pre-recorded videos, interactive code-alongs, and practical exercises before each live session. 

This allows live teaching to focus on applying concepts, solving problems, and discussing key ideas with academic staff and peers rather than introducing material for the first time.

Each course includes a weekly live session, typically delivered as a blend of practical exercises and discussions, lasting between one and two hours depending on the course. 

To maximise accessibility and participation, the schedule for these sessions is usually determined in consultation with each cohort through a pre-course availability survey.

Typical weekly schedule
  • Thursday to Tuesday: Materials released for the next week, students watch videos, complete at-home coding exercises and take part in discussion groups (takes about 8 hours of self-managed time).
  • Wednesday morning or afternoon: students come to a 1 to 2 hour online tutorial to work in pairs on programming tasks or other course activities, which solidify content they worked on over the past week.

Equipment and software needs

To study this online programme, you will need access to:   

  • a computer or laptop  
  • the internet  
  • the latest version of a web browser  

As an online student, you will have access to a range of software you can download to help you complete your coursework, including Microsoft Office 365. 

IT support is available if you have technical difficulties.  

IT and computing help

Opportunities to attend in person

Our programme is 100% online. Although you will not need to visit Edinburgh in person at any time during your study, we do very much welcome you to visit us if you find yourself in the area.

Graduation 

All students are offered the opportunity to graduate in person in Edinburgh, Scotland, at the completion of their studies.

Support for online students

Student life 

Even though you are not on campus in Edinburgh, there are lots of ways in which you can take part in student life and enjoy being part of our community.

Online Students website

Student representation 

Effective student representation is a vital part of our teaching and learning to enhance the student experience.

Programme student representatives will be nominated and will communicate regularly with the teaching programme about any student concerns. A staff student liaison committee (SSLC) will be held each semester to discuss student feedback.

The purpose of these meetings will be to discuss issues and good practice, but also to review feedback and work with students to monitor and improve the programme delivery and teaching content, as required. The student representatives will set the agenda and chair the SSLC meetings.

More information about students getting involved

Career opportunities

Graduates of the MSc Data Science for Health and Social Care are equipped with the technical, analytical and communication skills needed to work with complex health and social care data. 

The programme prepares students for careers across healthcare, academia, government, charities and the private sector, where there is increasing demand for professionals who can extract meaningful insights from data to improve services, inform policy and support evidence-based decision making.

Many graduates pursue roles involving statistical analysis, machine learning, health informatics, epidemiology or clinical research, while others continue to doctoral study or research careers. The programme also provides a strong foundation for professionals already working in health or social care who wish to develop advanced data science skills for career progression.

Example job roles

  • Data Scientist
  • Health Data Scientist
  • Health Data Analyst
  • Clinical Data Scientist
  • Health Informatics Specialist
  • Clinical Trials Data Manager
  • Public Health Intelligence Analyst
  • Business Intelligence Analyst (Healthcare)

Example sectors

  • NHS organisations (NHS England, NHS Scotland, NHS Digital)
  • Pharmaceutical and biotechnology companies
  • Intermediate Data Analyst roles in a student’s existing workplace

Professional links

The programme has strong links with organisations across healthcare, academia, government, and industry. These connections help ensure that our teaching reflects current practice and the evolving needs of the health and social care sector.

You will have the opportunity to undertake projects based on real-world challenges, with many dissertations developed as industry projects in collaboration with external organisations. These projects allow you to apply data science techniques to authentic datasets and practical problems while gaining experience of working with stakeholders beyond the University.

Throughout the programme, you will also benefit from guest lectures and seminars delivered by researchers and practitioners, providing insight into current developments, emerging technologies and career pathways within health and social care data science.

Further study

After completing this programme, you may wish to consider applying for a PhD or other research programme.

Applying for research degrees

Moving on to a PhD (advice from the University's Careers Service)

Graduate profiles

Holly

Degree: PgCert Data Science for Health and Social Care

Year of graduation: 2025

Current work: Senior Clinical Teaching Fellow

I had no prior knowledge of data software, but the modules on using Python and R for data analysis and visualisation were intuitive. The varied modules on topics from statistics to epidemiology gave me a breadth of knowledge and skills, and these were extremely useful for my successful application to the public health register training programme. I also found the online learning convenient to fit around my work, and supported by a very approachable teaching team.

Careers Service

Our Careers Service can help you to fully develop your potential and achieve your future goals. 

The Careers Service supports you not only while you are studying at the University, but also for up to two years after you finish your studies. 

With the Careers Service, you can: 

  • access digital resources to help you understand your skills and strengths
  • try different types of experiences and reflect on how and what you develop
  • get help finding work, including part-time jobs, vacation work, internships and graduate jobs
  • attend careers events and practice interviews
  • get information and advice to help you make informed decisions 

Visit the Careers Service website 

How to apply

You apply online for this programme. After you read the application guidance, select your preferred programme, then choose 'Start your application' to begin.

If you are considering applying to more than one programme, you should be aware that we cannot consider more than 5 applications from the same applicant.

When to apply

Programme start date Application deadline
13 September 2027 20 August 2027

We encourage you to apply as soon as possible so that we have enough time to process your application. This is particularly important if you are also applying for funding. Applications may close earlier than published deadlines if there is exceptionally high demand.

For an application to be reviewed, it must be a complete application by the application deadline with all supporting documentation uploaded, including references and transcripts.

If you already have evidence that you meet the English language entry requirements, such as via an approved English language test, please upload this evidence at the time of your application. If you have not already met your English language requirements, please upload that evidence as soon as you have it.

Application fee

There is no fee to apply to this programme.

What you need to apply

As part of your online application, you will need to provide: 

You will also need to submit some or all of the following supporting documents:

When you start your application, you will be able to see the full list of documents you need to provide.

References

You must submit one reference with your application.

Apply

Select the award, duration and delivery mode you want to study. Then select the start date you want to apply for.

After you apply

Once you have applied for this programme, you will be able to track the progress of your application and accept or decline any offers.

Checking the status of your application

We will notify you by email once we have made a decision. Due to the large number of applications we receive, it might take a while until you hear from us.

Receiving our decision

What to do if you receive an offer:

General enquiries

Adele Willoughby

Programme Administrator

  • Edinburgh Medical School

Contact details

Academic contact

Dr Brittany Blankinship

Programme Director

  • College of Medicine and Veterinary Medicine
  • University of Edinburgh

Contact details

Address

Usher Building
5-7 Little France Road
Little France
Edinburgh
EH16 4UX

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