MSc Health Data Science

Year of entry: 2021

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Course unit details:
Understanding Data and Decision Making

Unit code IIDS67622
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Division of Informatics, Imaging and Data Sciences
Available as a free choice unit? No


The applications in health data science are vast; each incorporating a different set of methods and technologies to be able to address the health problem. 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 and 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. This unit will be delivered in the context of a number of real-world case-studies drawn from research at the University of Manchester and NHS.


This unit aims to develop and integrate the strong healthcare acumen, problem-solving skills, communication and influencing skills with the technical and methodological skills required for a health data scientist.  It will provide the student with an understanding of the strategic importance of business intelligence and decision support in health. 

Learning outcomes

Indicative content for the unit includes:

  • Governance - Decision-making process
  • Data – sources, quality, technical, ethical and legal issues; linking data sources
  • Data visualisation – techniques, software and presentation style
  • Communication/presentation styles – requirements elicitation;
  • Organisational and change management
  • Risk management

Teaching and learning methods

This unit will consist of an engaging blend of lectures, exercise, discussions and case-studies designed to put theory into practice.  The enquiry-based learning approach will encourage discussion and debate allowing individuals to actively share knowledge.  This unit will follow the delivery template:

  • Essential ideas and theories will be introduced through on-line course material.  These will then be reinforced and built upon in the F2F time through a short talk and structured discussion.
  • Facilitated by a tutor, students will work in groups to analyse a particular case study related to the health-care delivery. As an endpoint students will produce a piece of work and discuss these with the wider group. 
  • Following each F2F session, students will be asked to reflect upon their learning

In total there will be three, two-day (equally spaced across the semester) F2F workshops that have lecture/structured discussion followed by problem-based workshops.  At the end of the workshops the students will be asked to present their work back to the group in a style for a non-technical audience (including the public).

 The unit will be embedded in real-world case-studies (published or on-going) that will look at various stakeholders across the patient pathway. 

Knowledge and understanding

  • Discuss the pathway from data collection, interpretation, analysis, visualization and use.
  • Evaluate the advantage and the technical, ethical and legal problems associated with the use of health data.
  • Know the range of data sources available for analysis and dicuss the characteristics.
  • Know how large datasets are created and used to support planning/commissioning, research/funding decision.
  • Understand the strategic importance of business intelligence, knowledge management, and decision support in healthcare.
  • Understand barriers when working with health data.  

Intellectual skills

  • Identify and appraise data sources used to support healthcare decision making.
  • Critically analyse a healthcare problem and provide a suitable strategy to address it.

Practical skills

  • Elicit information from various stakeholders using various practices.
  • Conduct a risk analysis focussed on a healthcare prolem.

Transferable skills and personal qualities

  • Communicate effectively both in written and verbal format to both non-technical and technical audiences (including the public)
  • work effectively as group.

Employability skills

Group/team working
Work effectively as a group
Oral communication
Communicate effectively both in written and verbal format to both non-tenchinical and technical audiences

Assessment methods

Method Weight
Written assignment (inc essay) 70%
Oral assessment/presentation 30%

Feedback methods

Online formative assessment and feedback to students is a key feature of the on-line learning materials for this unit.

Groups will be asked to discuss their work at key point throughout each workshop allowing for formative feedback from tutors and other students

Study hours

Scheduled activity hours
eAssessment 30
Lectures 18
Tutorials 24
Independent study hours
Independent study 78

Teaching staff

Staff member Role
Georgina Moulton Unit coordinator
Alan Davies Unit coordinator

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