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Unit of study_
OCMP5048: Visual Analytics
Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.
| Code | OCMP5048 |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
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Prerequisites:
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None |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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COMP5048 or COMP4448 |
| Assumed knowledge:
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Experience with data structures and algorithms as covered in COMP9123 or COMP9103 or COMP9003 or COMP2123 or COMP2823 or INFO1105 or INFO1905 (or equivalent UoS from different institutions) |
At the completion of this unit, you should be able to:
- LO1. select appropriate visual variables, space utilisation methods, and levels of organisation of visual components, to depict complex data
- LO2. select, apply, and modify visualisation methods suited to a given problem domain, in order to facilitate data analytic process through visual inspection
- LO3. understand basic computational concepts, techniques, and algorithms to produce good visualisation of abstract data
- LO4. understand the basic human-computer interaction principles, which influence the production of good/effective visualisation
Unit outlines
Unit outlines will be available 1 week before the first day of teaching for the relevant session.
- Semester 1a, 2023 [Online] - Online Program
- Semester 2b, 2023 [Online] - Online Program
There are no unit outlines available online for previous years.
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