Why study MSc Health Data Science (Online) at Exeter?
Master cutting-edge skills in the field of health-related data; a fast-growing area in which the UK excels in, with access to the largest biobanks, genomic and health service resources.
Tackle real-world challenges through hands-on research project opportunities within the NHS, pharmaceutical and health data companies.
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.
Build industry-ready skills with a strong focus on Python – a coding language used across the technology space.
Top 10 in the UK for our world-leading and internationally excellent Clinical Medicine research
Based on 4* + 3* research in REF 2021
Fully online programme
Learn from world-leading experts
Dedicated careers support
"Our Masters in Health Data Science attracts a uniquely diverse cohort, bringing together students from a range of backgrounds like maths, engineering, biosciences and medicine. I came from a mathematics background and moved into healthcare, whereas our Co-Programme Director, Mike Weedon, started in biosciences and learnt a variety of data science skills later on. This programme is designed for both paths which creates a rich and collaborative online environment."
Harry Green
MSc Health Data Science Online Co-Programme Director
Course content
Modern medical science is becoming increasingly driven by interdisciplinary teams making discoveries from analysing large datasets. The University of Exeter is leading the way with world-class research, data science-driven environments in genomics, diabetes, neuroscience and health services.
This course will introduce you to Python language, focusing on the analysis of health data with machine learning and statistics techniques. Additionally, you'll build your computing skills with Linux, manage databases using SQL, and collaborate on coding projects with GitHub. You'll also explore the core concepts of evidence-based medicine and learn how to apply Operational Research to improve health services planning and reconfiguration.
Our research projects are unique - you’ll have the opportunity to carry out a research project, working real-world health data with external partners including the NHS and companies involved in health data. The projects will be completed online using resources such as UK Biobank.
Please note that the module information displayed here is subject to change.
90 credits of compulsory modules.
Compulsory modules
Code
Module
Credits
Compulsory Choice 1
Computing Skills and Python
30
Research Design and Statistics
30
Stratified Medicine
30
Making a difference with Health Data
30
Data Science Research Project
60
HPDM206Z: Computing Skills and Python
Health data science is a complex field requiring a wide range of computing skills. For example, increasingly, many health datasets are hosted on cloud computing resources and requiring specialist software and multidisciplinary teams to access them. This module will start by teaching concepts in computing for Health Data Science. To extract meaningful information from such datasets, health data scientists often use computer programming languages to create bespoke analysis pipelines. Python is the most popular programming language for this task, making it a widely transferrable and employable skill.
This module assumes no prior knowledge of Python or any other computer coding language. We will be teaching Python from the ground up, starting with basic structures and objects available within Python, then developing more complex routines. When the fundamentals are established, you will learn how to manage and visualise data in Python. At the end of the course, you will learn how to perform machine learning tasks in Python, and come out of the module with general transferable computing and code-writing skills that will help you learn new languages quicker.
By the end of the module, you will have learned the following skills:
This module provides you with a broad introduction to research design and statistical modelling for data scientists. The module starts by introducing you to some fundamentals of research design, including the value and limitations of quantitative and qualitative data, and some key elements of study design, such as experiments, comparison groups and randomisation. It then introduces the different stages of a statistical investigation and emphasising the importance of problem formulation. The module highlights the benefits of exploratory data analysis based on descriptive statistics and graphs. You will also critically examine fundamental concepts at the heart of evidence-based medicine.
By undertaking this module you will gain critical insights into the diversity of methods and core concepts needed to conduct high quality quantitative health research. The aims of this module are to provide skills to design and carry out research studies that not only produce valid and reliable knowledge on important health and health service problems, but research findings which are useful to those working in health-related fields.
Genetic and phenotypic health data are becoming available in millions of people from around the world, through health care systems (including the NHS) and large-scale biobanks (e.g. UK Biobank). These data are being used to predict disease risk and health outcomes, and to separate (stratify) groups of individuals based on these features.
In this module you will learn about the sources of large-scale electronic healthcare data (including diagnoses, blood test results, and medication prescribing) and genomic data and their limitations. You will learn how these data are stored and used in disease prediction and classification, and the computational and statistical methodologies used to stratify individuals into groups at higher risk of disease, disease sub-types, and variable responses to treatment. You will use Python, statistical programming languages (R), database management systems (MySQL), and Linux command line tools.
You will also be taught fundamental concepts in human genetics that underpin common analyses of genetic data and learn how to interpret findings from these analyses. You will gain insight into how these findings can be used in drug development.
Theoretical sessions will be followed by practical workshops and assessments.
Health services are complex organisations that must coordinate their workforce and patient pathways to deliver high‑quality care both effectively and efficiently. In this module, you will be introduced to Operational Research (OR) in health, the discipline of using quantitative models to support decision‑making in complex health service environments. You will learn how OR methods related to time series forecasting, machine learning, optimisation, and discrete‑event simulation can be used to support the planning, evaluation, and reconfiguration of health services.
The module is code‑intensive. You will work extensively in Python 3, using libraries such as numpy, pandas, keras, tensorflow and specialist libraries for computer simulation. Through practical exercises and case studies drawn from real health service settings, you will develop hands‑on skills in building, analysing, and interpreting models for service delivery.
You will explore how to improve the quality and efficiency of health service logistics using forecasting, simulation, and optimisation methods. Typical questions addressed include:
The aim of this module will be to apply your data science skills developed in the first two terms of the MSc course to a real-life application in health. A range of project specifications will be developed drawing from the wide network of collaborating partner organisations in both commercial and health service sectors as well as internal research-oriented projects based at the university. These partner organisations include National Health Service organisations, as well as pharmaceutical companies and health data companies. From the selection of project options you will be asked to choose your preferred projects so that the work can be matched as closely as possible to your interests and needs. Oversight and supervision of project work will be provided on an on-going basis from both academic and workplace supervisors. The module will be assessed via performance during the project, a poster presentation and final report at the end of the project.
You will learn how to apply the skills learned from the preceding modules of the course to real life areas of interest in health data science. To bring together and integrate the different elements and skills against specified objectives within a project framework. You will experience what it means to work to address specific and focused objectives and to manage a project against a defined timeline. You will develop the skills necessary to clearly communicate the outputs on your work.
Harry is a lecturer in health data science. Harry comes from a background in pure mathematics, and moved towards medicine with a PhD in mathematical modelling of cardiac biophysics. He joined the medical school in 2017 and since then has been working as a data scientist focused on using genetics to further our understanding of chronic diseases: what causes them, and how to predict them. Harry has been teaching at universities since 2012, and has experience guiding students from a range of backgrounds, having taught on Engineering, Mathematics and Medical programmes.
Mike Weedon is a professor of bioinformatics and human genetics. He has been at the University since 2001. Mike has published over 300 papers on gene discovery and casual inference across a range of disease phenotypes.
Robin is a Senior Research Fellow in the Genetics of Complex Traits Group. His research focusses on understanding the genetic architecture of human traits using large population studies such as the UK Biobank and All of Us cohorts. His current work looks at developing statistical methods and analysis frameworks and pipelines for understanding the effects of rare genetic variants using large-scale whole genome sequence data.
A 2:2 with honours in a strongly numerate subject (e.g. computer science, mathematics, physics) or health/life sciences. Prior coding skills are not required.
Alternatively, we will consider applications where there is evidence of strong skills in maths, computing or engineering, but not necessarily a degree.
English language requirements
International students need to show they have the required level of English language to study this course.
To make it easier to budget, you don’t need to pay the total fee upfront. Instead, you can pay for each module as you are about to start studying it. You can pay for the whole year if you prefer, but the minimum payment is at least the cost of the module you are taking that term.
Find out more about the funding opportunities available to you, including the UK government postgraduate loan scheme.
What opportunities does an online Masters in Health Data Science lead to?
Who is this course for?
This course is suitable for anyone who is interested in pursuing a career or further study in health data science. We welcome students from computer science, maths, physics or engineering but who do not necessarily have any experience in biology or health – and students from health and life sciences that are keen and interested to expand their skillset into health-related data.
Employer-valued skills this course develops
The programme uses the Python programming language, one of the most desired computer programming languages by employers. The computing skills you develop will equip for a wide range of careers in healthcare and beyond. In a world increasingly driven by AI and big data analysis, experience with coding and machine learning will only become more and more valued by employers across the world.
Work-based learning
The majority of students do their project with an external provider – providing a chance to work in the real world with real health data. Project providers include those in the NHS, pharmaceutical industry and health data companies. Students have a wide choice of projects because we have more projects than students, a result of the outstanding reputation of the programme and the students. Students often continue working with their project providers after graduation.
Career paths (graduate destinations)
Exeter’s Masters in Health Data Science (Online) provides students with excellent careers opportunities. Students from the first two on-campus cohorts have obtained positions with employers in the NHS, including NHS Digital, the Office of National Statistics, Data science and AI companies. Find out more about our on-campus Masters in Health Data Science.
Dedicated careers support
You will receive support from our dedicated Career Zone team, who provide excellent career guidance at all stages of career planning. The Career Zone provides one-on-one support and is home to a wealth of business and industry contacts. Additionally, they host useful training events, workshops and lectures which are designed to further support you in developing your enterprise acumen. Please visit the Career Zone for additional information on their services.
The majority of students are based at our Streatham Campus in Exeter. The campus is one of the most beautiful in the country and offers a unique environment in which to study, with lakes, parkland, woodland and gardens as well as modern and historical buildings.
Located on the eastern edge of the city centre, St Luke's is home to Sport and Health Sciences, the Medical School, the Academy of Nursing, the Department of Allied Health Professions, and PGCE students.
Our Penryn Campus is located near Falmouth in Cornwall. It is consistently ranked highly for satisfaction: students report having a highly personal experience that is intellectually stretching but great fun, providing plenty of opportunities to quickly get to know everyone.
