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

  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. Create static, animated and interactive visualisations of data
  5. 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

 


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