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 2021/2

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 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

 


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