Online course
Health Data Science
- Qualification: PGCert
- Duration: 12 months
- Workload: Approx 15 hours per week
- Next enrolment: September 2021
- Fees: £5,000
Introduction
Become the health data science expert
Upskill to meet the growing need for people with the skills to derive insights from health data.
Health data science is a huge growth area with the explosion in the availability of data and computational power. This PGCert covers the full range of issues in health data science: from governance to epidemiology, from data management to statistical analysis.
Key features
Learn on the job
Combine your studies with your work and start making an impact from day one.
Build a global network
Learn from health and data colleagues in different settings across the world.
Practise new skills
Bring together the technical, modelling and contextual skills to be a successful health data scientist.
This degree is delivered using real-life case studies provided by the Health eResearch Centre
Benefits of our approach
- Focus on large and complex datasets.
- Encourage intellectual curiosity, creativity and critical thinking.
- Learn from a joint team of international leaders in the field.
- Conduct impactful work and advance healthcare delivery.
Key information
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Delivery
100% online course developed to fit around your lifestyle and circumstances.
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Duration
PGCert - 12 months, part-time.
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Enrolment dates
PGCert - Starts Sept 2021. CPD Data Engineering for Health Data Science - Sept 2021. CPD Systems and Engineering in Health Data Science - Nov 2021. CPD Maths and Statistics for Health Data Science - Feb 2022.
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How to apply
Applications are now open. Complete the contact form below and we'll email you with the details about the course or click Apply now at the top of the page.
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Workload
Approx 15 hours per week
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Course director
Dr Matthew Sperrin
Fees and funding
The course fee for the PG Cert for September 2021 is £5000.
There is an early application tuition fee discount of 10% (£500) applied to all applications received before Monday 26 April 2021.
The fee for each CPD unit is £1,250.
Entry requirements
We require an honours degree (minimum Upper Second) or overseas equivalent in one or the following subjects.
Mathematics or statistics, biological, medical, physical or computer sciences subjects, with sufficient evidence of the relevant units taken.
Contact us today
Course overview
Who this course is for
For the data professional who wants to add value to their team by upskilling in data knowledge, techniques and reporting by studying this flexible 100% online qualification while continuing to work and fitting this study around their work and other commitments.
Bring around a change in big health data reporting and meeting the need for more health data scientists in an increasingly data-led health environment.
What you will learn
- Bring together the technical, modelling and contextual skills to be a successful health data scientist.
- Apply these skills to real-world problems, harnessing the potential of health data.
- Understand the importance of the patient and the governance surrounding working in the healthcare environment.
- Use case studies provided by existing work and research at the Health eResearch Centre (HeRC) and focus on large and complex health datasets in environments that safeguard patient confidentiality.
Where and when you will study
This fully-online, part-time PGCert means you can study from anywhere in the world and fit it around your day-to-day life.
You will complete four taught units in 12 months and learn through a mix of interactive teaching and have plenty of opportunities to work collaboratively with your peers. You can choose to undertake single CPD units one at a time, or complete all four to receive your PGCert.
How it will benefit your career
- Design treatments and allocate resources based on analysis of health and related data.
- Report on data with more clarity and understanding and develop techniques to support your career.
- Use skills and knowledge acquired through the course in your job from day one to start advancing in your role and beyond.
- Practise applying your skills in real-life scenarios through our simulation platform, and be prepared for a range of situations.
Course units
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Data Engineering for Health Data Science
- Programming in Python
- Version control in Git
- Relational databases and SQL
This unit will equip you with fundamental techniques in data engineering, with a particular focus on programming in a transparent and reproducible fashion (using Python), use of version control, and interacting with relational databases. You'll explain the features of Python that support object-oriented programming and create functioning and well documented Python code.
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Understanding Health Data Science
- Health Information Systems and Technologies
- Data storage and retrieval technologies and data models and architecture
- System testing and quality assurance: ISO-standards and the system design cycle and methodologies
This unit aims to examine health systems and technologies and evaluate their impact on the delivery of healthcare, and to understand different approaches and methodologies to systems development, and how and when to apply them, in an international context. It will provide an opportunity for you to develop an understanding of health systems and technologies, and their implementation. In particular, the unit will cover the framework for handling patient data in a confidential and secure manner to ethical and quality standards that are appropriate for a modern health service.
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Maths and Statistics for Health Data Science
- Mathematics and Probability Theory
- The three ‘data science’ tasks, summary statistics, visualisations for data exploration, confidence intervals, hypothesis testing.
- Prediction and Causal Inference: Linear and logistic regression, assessing goodness of fit, confounding
The unit will begin with some required mathematical and probability theory, then will focus on the three key ‘data science tasks’ of description, prediction, and causal inference. Throughout, there will be a focus on articulating appropriate scientific/research questions, coding in R, and communicating results effectively (including through visualisations). Content may include:
Mathematics and Probability Theory: Rules of probability, conditional probability, discrete probability distributions, continuous probability distributions, statistical inference.
Description: The three ‘data science’ tasks, summary statistics, visualisations for data exploration, confidence intervals, hypothesis testing.
Prediction and Causal Inference: Linear and logistic regression, assessing goodness of fit, confounding, regression for causal estimation, understanding the difference between prediction and causal inference.
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Applied Health Data Science
- Governance, data sources, quality, technical, ethical and legal issues; linking data sources
- Data visualisation – techniques, software and presentation style and communication/presentation styles
- Organisational and change management and risk management
To improve the delivery of healthcare there is a need to maximise the potential of health data by turning it into useful intelligence to provide insights into past, current and future healthcare delivery. The role of the health data scientist is to be at the forefront of this and to influence decision-making for healthcare delivery by deriving understanding and significance from data.
To be able to do this it is critical to understand the decision-making governance and cycle; know how to elicit understanding of the clinical and public health objectives and processes; understand the different data sources available to support decision-making, and which are appropriate to use; understand which techniques and methodologies are most appropriate to investigate data; and know how to communicate and visualise results /ideas to various stakeholders (often from a non-technical background; including the public); and determine how this will impact on healthcare. This unit will integrate technical and methodological skills with each of these issues and apply them to uses of ‘big data’ in healthcare.
Course structure
This PGCert will upskill you in the fundamentals of health data science. It will equip you with a basic competency in this area so you can understand the operational and governance issues around managing health data, the international context, and fundamental skills in mathematics, statistics and epidemiology to design and analyse simple studies to address business or research questions.
With a focus on highly applicable skills and knowledge, you will also learn through interactive, state-of-the-art simulations that allow you to experience and practice real-world scenarios. You will complete four taught units in year one. You’ll learn through a mix of interactive teaching and have plenty of opportunities to work collaboratively with your peers.
Course learning aims
In an increasingly data-led health environment you'll bring around a change in big health data reporting and meet the need for more health data scientists.
By studying this course you'll be able to describe how healthcare systems are organised, funded and regulated and how these relate to health data science. You'll be able to demonstrate a critical understanding the flow of data/information and knowledge and its use across the health and social care system.
You'll critically evaluate systems and technologies in relation to current and future thinking around health systems and review the range of systems used to collect, produce and store data.
You'll examine the key issues in information governance, cyber-security and issues of privacy and confidentiality and explain the types of systems and components; network and communication protocols; and databases.
You'll be able to explain the different systems/software development and design methodologies (e.g., agile) and how these fit in the broader cycle of development, deployment and maintenance of healthcare systems.
You'll be able to articulate the importance of interoperability. You'll also be able to describe how data is collected and created to support clinical, research, direct care and commissioning decisions.
You'll be able to demonstrate a critical understanding of the epidemiological and statistical principles used to design studies that provide robust evidence to underpin research, policy, or decision-making.
You'll be able to demonstrate a critical understanding of the flow of data/information and knowledge and its use across the health and social care system, as well as critically evaluating systems and technologies in relation to current and future thinking around health systems.
Teaching and learning
The PGCert program comprises four modules of 15 credits.
- Data Engineering for Health Data Science will cover introductory programming and data management.
- Systems and Technologies in Health Data Science introduces issues around coding, governance, linkage and workflows.
- Mathematics and Statistics for Health Data Science covers foundational techniques in mathematics, statistics and probability needed to analyse simple studies.
- Applied Health Data Science uses real examples, particularly drawn from electronic health records, to cover epidemiological concepts, applied data analysis, and visualisation.
Coursework and assessment
The course is delivered entirely via online learning, including assessment. Each module of the course runs over 10 weeks, with a nominal 15 hour per week of student work. There are short podcasts, activities and quizzes, interspersed with discussion activities, where you'llcontribute to a discussion board.
The course has been designed to fulfil the aspirations and needs of all sectors that by developing a world-class health informatics workforce that is integral to multi-professional teams with the technical, methodological and communication skills to be able to deliver high-quality, innovative healthcare delivery, in a range of settings.
Each week during a 10-week module consists of:
- An overview of the material, presenting the learning objectives for the week (video).
- Explanatory material (3h of student activity/week) in the form of video lectures, papers/articles, the course text, and links to further resources.
- Exercises (4h/week). These are a mixture of formative/summative tasks with discussion of these required in the discussion boards. Feedback will be given in the weekly tutorial.
- Discussion (2h/week). Students are encouraged to discuss the exercises and material in the forums where tutors will facilitate peer learning, providing feedback/input where necessary.
- Formative Questionnaire. This is to gather students' questions and highlight misconceptions ready for the tutorial.
- Tutorial (1h/week). Students will video conference with their tutor in groups of 6-8 to discuss and give/receive feedback.
There is also private study of 5 hours per week consisting of:
- Revision
- Coursework
- Further practice (after the tutorials)
- Independent/further study
Admissions information
From your initial expression of interest right through to graduation, you’ll receive all the support you need. We can support you with enrolment and subject assistance, administrative logistics and fee options, online learning skills, workload management and special circumstances including a possible professional entry route.
Entry requirements
Academic entry qualification overview
We require an honours degree (minimum Upper Second) or overseas equivalent in one or the following subjects.
Mathematics or statistics, biological, medical, physical or computer sciences subjects, with sufficient evidence of the relevant units taken.
English language
International students must demonstrate English proficiency through a secure and approved testing system. We ask for English language proof if you are 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 : 6.5 overall (and a minimum of 6 for writing) or equivalent
- TOEFL: 90 internet-based (with 25 in each component)
English language test validity
Relevant work experience
Application and selection
How to apply
If you have any questions regarding the course, please contact us on studyonline@manchester.ac.uk
Advice to applicants
The usual sequence of the modules will be:
- Data Engineering for Health Data Science (September)
- Systems and Technologies in Health Data Science (November)
- Mathematics and Statistics for Health Data Science (February)
They can each be taken as CPD modules and can be taken in any order.
The module Applied Health Data Science has the previous three modules as prerequisites and cannot be taken as CPD. It is possible to transfer from CPD to the PGCert by completing the remaining modules.
Scholarships and bursaries
Additional cost information
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).
Regulated by the Office for Students
The University of Manchester is regulated by the Office for Students (OfS). The OfS aims to help students succeed in Higher Education by ensuring they receive excellent information and guidance, get high quality education that prepares them for the future and by protecting their interests. More information can be found at the OfS website.
You can find regulations and policies relating to student life at The University of Manchester, including our Degree Regulations and Complaints Procedure, on our regulations website.