Aims and Fit of Module

Data visualisation is a tool for both the exploration of a data set and for the clear explanation of the findings. In this module students will gain practical experience in data visualisation methods for analysing the structure and dependencies of data sets. Students will also study techniques for creating effective visual data presentations.

Learning outcomes

A. Use data visualisation methods and techniques to analyse a given data set.

B. Use software tools to analyse a given data set.

C. Select appropriate analysis and visual encoding methods for a given data type.

D. Design an effective visualisation to highlight particular features of a given data set.

E. Critically evaluate the effectiveness of a given data visualisation.

F. Create visualizations using interactive web graphics programming in JavaScript, and D3.js.

G. Describe the fundamentals of 2-D and 3-D graphics

Method of teaching and learning

The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments.

This module is taught using a combination of lectures, seminars and computer lab sessions. In the computer lab sessions, students will gain experience with software tools for data visualisation.
During the semester students will complete two projects, each consisting of an exploratory analysis of a chosen data set and a visual presentation of the findings. Assessment of projects will involve a peer-review element.