Information Visualisation

Module summary

Module code: COMP1709
Level: 5
Credits: 15
School: Liberal Arts and Sciences
Department: Computing and Mathematical Sci.
Module Coordinator(s): Christopher Walshaw

Specification

Pre and co requisites

Basic programming skills and basic understanding of data manipulation.

Aims

This course aims to prepare students to work in the area of information visualisation by introducing them to the relevant technologies and principles of visualisation in data exploration.

This course will equip students with the skills required to identify and analyse trends and patterns in datasets using visual representations.


Learning outcomes

On successful completion of this module a student will be able to:

1 Identify and discuss fundamental concepts related to visualisation.
2 Demonstrate an understanding of different types of information visualisation and identify appropriate types of visualisation for various types of data.
3 Design, implement and evaluate interactive visualisation systems.
4 Apply visualisation tools and techniques to obtain insight from datasets.

Indicative content

This course will cover a range of subject areas, including but not limited to:

- Fundamental concepts in information visualisation.
- Good design practices in information visualisation.
- Different types of information visualisation and the options for using them.
- Interactive tools for information visualisation and dashboards. - Visual analytics for identifying trends and patterns in datasets.
- Practical experience of data exploration.

Teaching and learning activity

Each week students will attend a 1-hour lecture (33%) and a 2-hour lab (67%).

In the lectures, students will be introduced to the theoretical and technical concepts needed to design and implement different types of visualisations and carry out visual analytics.

Assessment

Coursework: 100% weighting, 40% pass mark.
Learning Outcomes: 1, 2, 3 & 4.
Outline Details: Technical documentation and report to present the process, implementation and evaluation of a visual data exploration for an unknown dataset.

Formative Assessment: Feedback on practical exercises in the weekly supervised labs will form part of the formative assessment. In addition there will be a coursework surgery to address common issues and questions around the summative assessment.