Module manager: David Wong
Email: D.C.Wong@leeds.ac.uk
Taught: 1 Dec to 31 Jan View Timetable
Year running 2024/25
First degree in a relevant subject e.g. Social Sciences, STEMM, Nursing (or equivalent) 2:1 OR previous work experience (minimum 2 years) of handling and/or analysing data IELTS 7 – minimum of 6.5 in each component
This module is not approved as an Elective
This module introduces students to visualisation as technique for communication of and interaction with health data. Students will learn the key principles of data visualisation and gain the knowledge required to determine appropriate visualisations for different communication and interaction scenarios in the health domain. Students will gain familiarity with and practical experience of the data visualisation pipeline, from the selection and ‘wrangling’ of health data to the generation of static and interactive visual representations.
The purpose of the module is to:
- Introduce students to the key principles of data visualisation as a technique for communicating and interacting with health data.
- Enable students to select and generate appropriate visual representations of health data to communicate with different stakeholders.
- Enable students to critically appraise the use of visual representations in the health domain.
By the end of the module, students will:
1. Describe and apply the data visualisation pipeline.
2. Communicate complex information graphically, using appropriate software to generate static and interactive visualisations from publicly available health data. (e.g , https://digital.nhs.uk/data-and-information/publications/statistical/practice-level-prescribingdata/ presentation-level-july-2017 )
3. Critically evaluate the applicability of different visual representations to specific communication and interaction scenarios in the health domain.
4. Articulate and critically appraise the different ways in which data visualisation is used in health domain.
- Principles of data visualisation: including colour, form and spacing.
- Data wrangling: including filtering, transformations, aggregation.
- Static visualisation: including static line plots and histograms.
- Interactive visualisation: including selection, panning and zooming.
- Applications of health data visualisation: including clinical decision making and public health surveillance.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 14 | 1 | 14 |
| Practical | 4 | 3 | 12 |
| Private study hours | 124 | ||
| Total Contact hours | 26 | ||
| Total hours (100hr per 10 credits) | 150 | ||
124 hours private study. Students will be expected to spend time reading relevant literature and consolidating the material. They will also be expected to practise the practical visualisation techniques taught in class.
In class discussion and practical work will provide opportunities for rapid face-to-face feedback from the tutors.
Students will have the opportunity to submit an outline plan of their report, including sample visualisations, for formative feedback.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | 2,000 words project report | 100 |
| Written Work | Formative, 500 word outlline | 0 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 02/10/2024
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