CMT212: Visual Communication and Information Design
|School||Cardiff School of Computer Science and Informatics|
|External Subject Code||I100|
|Number of Credits||20|
|Language of Delivery||English|
|Module Leader||Dr Martin Chorley|
Outline Description of Module
On completion of the module a student should be able to
1. Describe and discuss the theory behind visualisation design
2. Critically analyse visualisations of data
3. Examine and explore data to find the best way it can be visually represented
4. Apply statistical methods to data
5. Access web APIs and data sources, retrieve and manipulate data
6. Create static, animated and interactive visualisations of data
How the module will be delivered
Theoretical material and practical demonstrations will be delivered via online videos, to be watched by students prior to the weekly contact sessions.
Weekly contact sessions will include a mixture of activities reinforcing and expanding upon the theoretical concepts introduced online. Laboratory classes will allow students to practice implementation of the practical skills taught.
Skills that will be practised and developed
Use of appropriate tools for data analysis and visualisation
Critical analysis of visualisation.
How the module will be assessed
There are 2 points of assessment in this module.
The first assessment examines the students understanding of visualisation theory, covering learning outcomes LO1 and LO2 by asking them to critically assess a number of visualisations.
The second assessment tests the students ability to retrieve, analyse and present conclusions from datasets, covering learning outcomes LO3, LO4, LO5 and LO6.
All assessments will allow the student to demonstrate their knowledge and practical skills and to apply the principles covered online and in contact sessions.
The potential for reassessment in this module is an individual 100% coursework during the summer.
|Written Assessment||70||Data Analysis Visualisation Creation||N/A|
|Written Assessment||30||Visualisation Analysis||N/A|
Use of appropriate software tools and libraries for data analysis and visualisation
Python: Pandas, Scipy, Numpy, Matplotlib
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
Murray, S. 2013, Interactive Data Visualization for the Web – O’Reilly
McKinney, W. 2012, Python for Data Analysis – O’Reilly
Boslaugh, S., Watters, P. A., 2008. Statistics in a Nutshell. O’Reilly
Suda, B., A Practical Guide to Designing with Data, - fivesimplesteps