PGCert Clinical Data Science

For entry in the academic year beginning September 2027, the tuition fees are as follows:

• PGCert (full-time)
  UK students (per annum): £5,600
  International, including EU, students (per annum): £12,700

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

All students should normally be able to complete their course without incurring additional study costs over and above the tuition fee. Further information can be found in the University's Policy on additional costs (PDF document, 91KB).

Some NHS managed machines can make it difficult to access certain online learning materials. A non-NHS managed desktop or laptop is recommended.

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).

NHSE-funded places

Fully funded places may be available for this course through NHS England (NHSE).

Applications must be made directly to NHSE, not via the University.

Further information on eligibility for funded places and how to apply can be found on the National School of Healthcare Science website.

Self-funded places

We also accept applications from self-funded applicants via the standard University of Manchester application process.

See the Application and selection tab for more details.

In order to make use of data once it has been analysed, findings need to be easily communicable for wider clinical teams to capitalise on these insights. This is often achieved through the medium of data visualisation, infographics and interactive digital tools (e.g. data dashboards). Those communicating (telling stories with data) should be able to use appropriate visualisations including graphs and plots based on the properties of their data and the message they are trying to convey. An understanding of the theories of visualisation and knowledge of appropriate tools is needed to do this well without unintentionally misleading or confusing the intended recipient. Data visualisation skills are also an integral part of initial data exploration.

The unit aims to:

• Equip students with the knowledge and tools to use data visualisation for both the exploration and explanation of data
• Impart experience using tools to create data visualisations, including interactive data visualisations and digital dashboards
• To communicate a narrative about data to clinical and patient stakeholders adjusting the approach depending on the audiences needs
• To understand the theories of designing good visualisations that communicate the intended message clearly without misleading or confusing the target audience

This unit will cover the following indicative content:

• Exploring data using data visualisation
• Exploratory vs. explanatory data visualisation
• Types of visualisation (e.g. scientific, illustrative, infographic specialist)
• Fundamental theories of data visualisation and communication
• Representing uncertainty in visualisation
• Good design principles and understanding of related psychological theories (e.g. attention, gestalt principles)
• Producing plots/graphs using data visualisation libraries in R and Python (e.g. ggplot, matplotlib)
• Using and creating interactive visualisations
• Using data dashboards to communicate information (e.g. Tableau, RShiny)
• Decision support systems
• Creating accessible visualisations for patients and the public and those with special requirements

The unit will be delivered online making use of workshops, lectures, videos, interactive online activities and group work.

LO1: Describe the psychological theories related to attention and processing of visual stimuli

LO2: Discuss the best design practices for presenting data with visualisations

LO3: Recognise the difference between using data visualisations for exploration and for explanation

LO4: Recognise and select appropriate visualisations to communicate to different audiences with differing levels of technical and clinical understanding

LO5: Interpret characteristics of data by using data visualisations

LO6: Practice plotting data using appropriate visualisation types, including static and dynamic (interactive) forms of visualisation using modern graph libraries in languages like R and Python

LO7: Build interactive data dashboards using software (e.g. Tableau, RShiny) to present data related to a topic or theme for subsequent decision making and data exploration

LO8: Experience 'team science' to solve problems collaboratively through group work

Assessment task	Length	Weighting within unit
Individual recorded presentation	10 minutes	100%

 

Formative assessment and feedback to students is a key feature of the on-line learning materials for this unit and is provided through self-directed learning activities in the interactive notebooks. 

• Davies, A., Mueller, J (2020) Developing Medical Apps and mHealth Interventions: A Guide for Researchers, Physicians and Informaticians. Switzerland: Springer
• Wilkinson, L (2005) The Grammar of Graphics. New York: Springer
• Knaflic, CN (2015) Storytelling With Data: A Data Visualization Guide For Business Professionals. New Jersey: Wiley