INFORMATION VISUALIZATION

CSE5INV

2021

Credit points: 15

Subject outline

Information Visualization is the study of presenting information in a graphical format to facilitate effective decision making. In this subject, you will learn the theories and methodologies to visualize abstract data (numerical or non-numerical) for human cognition. Topics covered include histograms, scatter plots, hierarchical data visualization, surface plots, tree maps and parallel coordinate plots etc. In particular, you will learn the representation of temporal and spatial data, networks and trees, and textual data. You will also learn the principles and techniques on visualization design and evaluation.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorFei Liu

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

Information Visualization

Resource TypeRecommended

Resource RequirementN/A

AuthorSpence, R.

Year2014

Edition/Volume3rd ed

PublisherSpringer

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Introduction to Information Visualization

Resource TypeRecommended

Resource RequirementN/A

AuthorBenoit, G.

Year2019

Edition/VolumeN/A

PublisherRowman & Littlefield Publishers

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Design and create the visual representation for numerical data sets using various visualization tools and techniques.
02. Identify and critique techniques and practices in non-numerical data representation.
03. Critique and evaluate the suitability of a specific technique for specific types of data sets.
04. Critically assess a visualization representation and make justified recommendations

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorFei Liu

Class requirements

Computer LaboratoryWeek: 32 - 43
One 2.00 h computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.

LectureWeek: 30 - 42
One 2.00 h lecture per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Assignment 1 Visualization project for numerical data sets (1500-word equivalent)

N/AN/AN/ANo25 SILO1, SILO3, SILO4

Assignment 2 Visualization project for textual data sets (1500-word equivalent)

N/AN/AN/ANo25 SILO2, SILO3, SILO4

One 2-hour Examination (2000 words equivalent)

N/AN/AN/ANo50 SILO1, SILO2, SILO3, SILO4