2019/20 Undergraduate Module Catalogue

COMP3736 Information Visualization

10 creditsClass Size: 150

Module manager: Roy Ruddle
Email: R.A.Ruddle@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2019/20

Pre-requisites

COMP2811

User Interfaces

This module is not approved as a discovery module

Module summary

Covers the principles of visualisation in decision making and knowledge discovery. You will learn how to design new visual representations and how to modify and apply existing tools to gain new insight about data.

Objectives

On completion of this module, students should be able to:
- Understand the use of visualization in decision-making and knowledge discovery;
- Understand core technical concepts including data and processing models;
- Understand salient features of human perceptual and cognitive processing;
- Design new visual representations appropriate for a given task;
- Modify and apply visualization tools to obtain insight about a given dataset.

Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
-understand and demonstrate coherent and detailed subject knowledge and professional competencies some of which will be informed by recent research/scholarship in the discipline;
-deploy accurately standard techniques of analysis and enquiry within the discipline;
-demonstrate a conceptual understanding which enables the development and sustaining of an argument;
-describe and comment on particular aspects of recent research and/or scholarship;
-appreciate the uncertainty, ambiguity and limitations of knowledge in the discipline;
-make appropriate use of scholarly reviews and primary sources;
-apply their knowledge and understanding in order to initiate and carry out an extended piece of work or project;

Syllabus

- Overview of visualization, scivis, infovis, visual analytics; big data;
- Visual design, examples of both appropriate and inappropriate representation;
- Perception: the eye, light and colour, pre-attentive processing, gestalt phenomena;
- Cognition, tasks and knowledge, semiotics of representations;
- Different types of data, and the link between data, task, and representation;
- Processing technologies, in particular the pipeline model, and web-based approaches;
- Visualization as process, with schneiderman's mantra and the need for interaction;
- Visual analytics for finding patterns in huge datasets;
- Practical visualization using tools such as excel and Tableau (http://www.tableausoftware.com/data-visualization-software; provided through the Tableau for teaching program.

Teaching methods

Delivery type	Number	Length hours	Student hours
Laboratory	9	1.00	9.00
Lectures	18	1.00	18.00
Class tests, exams and assessment	1	2.00	2.00
Private study hours			71.00
Total Contact hours			29.00
Total hours (100hr per 10 credits)			100.00

Private study

Taught session preparation: 20 hours
Taught session follow-up: 20 hours
Self-directed study: 13 hours
assessment activities: 23 hours

Opportunities for Formative Feedback

Attendance and formative assessment.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	Design exercise and report	50.00
Total percentage (Assessment Coursework)		50.00

This module is re-assessed by exam only.

Exams

Exam type	Exam duration	% of formal assessment
Open Book exam	1 hr 30 mins	50.00
Total percentage (Assessment Exams)		50.00

This module is re-assessed by exam only.

Reading list

The reading list is available from the Library website

Last updated: 05/11/2019 08:50:02

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