CMT218: Data Visualisation
| School | Cardiff School of Computer Science and Informatics |
| Department Code | COMSC |
| Module Code | CMT218 |
| External Subject Code | 100366 |
| Number of Credits | 20 |
| Level | L7 |
| Language of Delivery | English |
| Module Leader | Dr Martin Chorley |
| Semester | Spring Semester |
| Academic Year | 2020/1 |
Outline Description of Module
The aim of this module is to give you an understanding of the processes and tools required to create interactive visualisations and explanations of data. The module will allow you to critically appreciate correct visualisations, and to identify biased or manipulated interpretations. It will cover the practical skills required to create visualisations using tools such as Python and JavaScript, while also examining the theory of design required
On completion of the module a student should be able to
- Describe and discuss the theory behind visualisation design
- Critically analyse visualisations of data
- Examine and explore data to find the best way it can be visually represented
- Create static, animated and interactive visualisations of data
- Critically reflect upon and discuss the merits and shortcomings of their own visualisation work
How the module will be delivered
Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include: • on-line resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes), • on-line interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings) • face to face small group sessions (e.g. help classes, feedback sessions)
Skills that will be practised and developed
Use of appropriate tools for data analysis and visualisation
Critical analysis of visualisation.
JavaScript and Python for data access, manipulation, statistical analysis and visualisation
How the module will be assessed
A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.
Assessment Breakdown
| Type | % | Title | Duration(hrs) |
|---|---|---|---|
| Written Assessment | 70 | Data Analysis Visualisation Creation | N/A |
| Written Assessment | 30 | Visualisation Analysis | N/A |
Syllabus content
Encoding theory
Visualisation theory
Visualisation history
Current trends in visualisation
Use of appropriate software tools and libraries for data analysis and visualisation
Python: Pandas, Scipy, Numpy, Matplotlib, Seaborn, Altair, Bokeh
JavaScript: D3, Plotly, Highcharts
Retrieving and storing data (JSON, csv) using JavaScript and Python
Visualisation development
Essential Reading and Resource List
Please see Background Reading List for an indicative list.
Background Reading and Resource List
Tufte, E, The Visual Display of Quantitative Information – Graphics Press
Cairo, A, The Functional Art – Pearson
Cairo, A, The Truthful Art - Pearson
Murray, S. 2013, Interactive Data Visualization for the Web – O’Reilly
McKinney, W. 2012, Python for Data Analysis – O’Reilly
Suda, B., A Practical Guide to Designing with Data, - fivesimplesteps
Yau, N. Visualize This - Wiley
Yau, N. Data Points - Wiley
Gemignani Z & Gemignani C. Data Fluency – Wiley
Meirelles, I. Design for Information: an introduction to the histories, theories and best practices behind effective information visualisations – Rockport
Munzner, T. Visualization Analysis and Design – CRC Press
Drucker, J. Graphesis: Visual Forms of Knowledge Production – Harvard University Press