32146 Data Visualisation and Visual Analytics
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): (31250 Introduction to Data Analytics AND 48024 Programming 2) OR 32130 Fundamentals of Data Analytics OR ((26776 Foundations of Business Analytics OR 22804 Business Analytics Foundations))
Description
This subject covers the core data visualisation and visual interaction (or navigation) technologies that support the visual analytics and decision-making processes. Students study the latest data visualisation articles and the practice of cutting-edge data visualisation and visual analysis software. The subject provides an essential understanding of the procedure (loop) and the methodology of visual data analytics. It discusses the human involvement (or input) in the loop of analytical reasoning facilitated by interactive visual interfaces. On successful completion of this subject, students are capable of designing and evaluating various advanced visualisation interfaces that can be directly applied into the loop of visual data mining or visual analytics to enable them to become data visualisation designers and visual data analysts.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Recognise, compare and apply a range of static data visualisation techniques. (D.1) |
|---|---|
| 2. | Apply interactive data visualisation techniques. (D.1) |
| 3. | Design and create data visualisations for particular social contexts that are appropriate for specific users. (C.1) |
| 4. | Design and create data visualisations that facilitate the discovery and presentation of data-driven stories. (C.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:
- 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
- 2.3. Application of systematic engineering synthesis and design processes.
- 3.3. Creative, innovative and pro-active demeanour.
Teaching and learning strategies
This subject is comprised of an introductory lecture (week 1) and workshops (weeks 2 to 12). Each week of workshops is self-paced through the learning management system. You are expected to complete a range of different learning activities throughout the week. Activities provide opportunities to learn, apply and discuss the knowledge gained in a practical manner. Feedback is provided from both peers and teaching staff throughout the activities. You are encouraged to actively provide feedback and interact with staff and students. The workshops will allow you to interact with staff and students, ask questions and receive clarification and feedback.
Content (topics)
- Introduction to data (attributes, relationships, behaviours)
- Visual representations of data, information and knowledge
- Visual querying, interaction and exploration
- Textual data visualization
- Zoomable interfaces, “Focus+Context” navigation
- Hypermedia (web) visualisation
- High-dimensional visualization
- Introduction of visual data analytics
- Behavior-driven data visualization
- Evaluation, visualisation and interaction
Assessment
Assessment task 1: Compare and evaluate different visualisations of a data set
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
|---|---|
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 30% |
Assessment task 2: Create a data visualisation of a dataset that includes a range of complex data types
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
|---|---|
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 30% |
Assessment task 3: Create an interactive data visualisation that enables users to discover and communicate data-driven stories
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
|---|---|
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 40% |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
The subject does not have a set textbook. Weekly readings and other resources are provided on the subject site on Canvas.
A list of recommended references is provided below.
References
- Card, S. K., MacKinlay, J. D., Shneiderman, B., (2000) Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann, ISBN 1-55860-533-9
- Colin Ware (2000) Information Visualization: Perception for Design, Morgan Kaufmann, ISBN 1-55860-511-8
- Geroimenko, V. & Chen, C., (2002) Visualizing the Semantic Web, Springer-Verlag, London, ISBN 1-85233-576-9
- Chaomei Chen (1999) Information Visualization and Virtual Environments, Springer-Verlag, London, ISBN 1-85233-136-4
- Simoff, S., Bohlen, M., Mazeika A. (2008) Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (Lecture Notes in Computer Science / Information Systems and Applications), Springer
- Huang, M. L., Huang, W. (2013) Innovative Approaches of Data Visualization and Visual Analytics. IGI Global, ISBN13: 9781466643093
Other resources
Canvas provides online support for the learning of this subject.
Students are also expected to regularly read the class announcements sent via Canvas or UTS email throughout the semester.
Main software used in this subject include Microsoft Excel and Tableau.