CMT212: Visual Communication and Information Design

School Cardiff School of Computer Science and Informatics
Department Code COMSC
Module Code CMT212
External Subject Code I100
Number of Credits 20
Level L7
Language of Delivery English
Module Leader Dr Martin Chorley
Semester Spring Semester
Academic Year 2018/9

Outline Description of Module

The aim of this module is to give students an understanding of the processes and tools required to create interactive visualisations and explanations of data. The module will allow students to appreciate correct visualisations, and to identify biased or manipulated interpretations. It will focus on 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

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.
JavaScript and Python for data access, manipulation, statistical analysis and 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.

 

 

 

Assessment Breakdown

Type % Title Duration(hrs)
Written Assessment 70 Data Analysis Visualisation Creation N/A
Written Assessment 30 Visualisation Analysis N/A

Syllabus content

?Visualisation theory
Use of appropriate software tools and libraries for data analysis and visualisation
Python: Pandas, Scipy, Numpy, Matplotlib
JavaScript: D3
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
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

 


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