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 | 2023/4 |
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
This module will be delivered through a combination of lectures, supervised lab sessions, example classes and tutorials, as appropriate.
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.
Students will be provided with reassessment opportunities in line with University regulations.
Assessment Breakdown
| Type | % | Title | Duration(hrs) |
|---|---|---|---|
| Written Assessment | 40 | Visualisation Analysis | N/A |
| Written Assessment | 60 | Data Analysis Visualisation Creation | 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