Data Visualisation and Communication

IIDS69032 Data Visualisation and Communication 15 Semester 2 Postgraduate Taught Level 7 Alan Davies Unit coordinator Frances Hooley Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 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. 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: <ul> <li style="margin-bottom:11px">Equip students with the knowledge and tools to use data visualisation for both the exploration and explanation of data</li> <li style="margin-bottom:11px">Impart experience using tools to create data visualisations, including interactive data visualisations and digital dashboards</li> <li style="margin-bottom:11px">To communicate a narrative about data to clinical and patient stakeholders adjusting the approach depending on the audiences needs</li> <li style="margin-bottom:11px">To understand the theories of designing good visualisations that communicate the intended message clearly without misleading or confusing the target audience</li></ul> LO1: Describe the psychological theories related to attention and processing of visual stimuliLO2: Discuss the best design practices for presenting data with visualisationsLO3: 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 understandingLO5: Interpret characteristics of data by using data visualisations LO5: Practice plotting data using appropriate visualisation types, including static and dynamic (interactive) forms of visualisation using modern graph libraries in languages like R and PythonLO6: 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 LO7: Experience 'team science' to solve problems collaboratively through group work This unit will cover the following indicative content:<ul> <li style="margin-bottom:11px">Exploring data using data visualisation</li> <li style="margin-bottom:11px">Exploratory vs. explanatory data visualisation </li> <li style="margin-bottom:11px">Types of visualisation (e.g. scientific, illustrative, infographic specialist) </li> <li style="margin-bottom:11px">Fundamental theories of data visualisation and communication</li> <li style="margin-bottom:11px">Representing uncertainty in visualisation</li> <li style="margin-bottom:11px">Good design principles and understanding of related psychological theories (e.g. attention, gestalt principles)</li> <li style="margin-bottom:11px">Producing plots/graphs using data visualisation libraries in R and Python (e.g. ggplot, matplotlib)</li> <li style="margin-bottom:11px">Using and creating interactive visualisations</li> <li style="margin-bottom:11px">Using data dashboards to communicate information (e.g. Tableau, RShiny)</li> <li style="margin-bottom:11px">Decision support systems</li> <li style="margin-bottom:11px">Creating accessible visualisations for patients and the public and those with special requirements</li></ul> The unit will be delivered online making use of workshops, lectures, videos, interactive online activities and group work. 0 Other 100% <table border="1" cellpadding="1" cellspacing="1" style="width:500px;"> <tbody> <tr> <td>Assesment task</td> <td>Length</td> <td>Weighting within unit</td> </tr> <tr> <td>1. Group presentation of data dashboards x2 to clinical and patient stakeholder representative panel</td> <td>N/A</td> <td>30%</td> </tr> <tr> <td>2. Short individual report</td> <td>1000 words</td> <td>70%</td> </tr> </tbody></table>  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.  N <ul> <li style="margin-bottom:11px">Davies, A., Mueller, J (2020) Developing Medical Apps and mHealth Interventions: A Guide for Researchers, Physicians and Informaticians. Switzerland: Springer</li> <li style="margin-bottom:11px">Wilkinson, L (2005) The Grammar of Graphics. New York: Springer</li> <li style="margin-bottom:11px">Knaflic, CN (2015) Storytelling With Data: A Data Visualization Guide For Business Professionals. New Jersey: Wiley</li></ul> 0 0 150