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 2017/8

How the module will be assessed

Coursework:  the coursework will allow students to demonstrate their knowledge and practical skills and to apply the principles taught in lectures.

The potential for reassessment in this module

Data Analysis & Visualisation Coursework resit only


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.

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 also 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

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


Assessment Breakdown

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

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. 2017,  2nd ed Interactive data visualization for the web : an introduction to designing with D3 – O’Reilly

McKinney, W. 2017 2nd ed  Python for data analysis : data wrangling with pandas, NumPy, and IPython – O’Reilly

Boslaugh, S., Watters, P. A., 2012. 2nd ed Statistics in a Nutshell. O’Reilly

Suda, B., A Practical Guide to Designing with Data, - fivesimplesteps


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:



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