MATH 2014 Visual Analytics
Credit Points 10
Legacy Code 301109
Coordinator Zhonglin Qu Opens in new window
Description This subject introduces the fundamentals and technologies of visual analytics to understand big data. It covers major concepts of information visualisation, human computer perception and methods for visual data analysis. Students will learn knowledge and skills for identifying suitable visual analytics techniques, methods and tools for handling various data sets and applications. The subject provides students with opportunities to explore novel research in visual analytics and visualisation.
School Computer, Data & Math Sciences
Discipline Statistics
Student Contribution Band HECS Band 1 10cp
Check your HECS Band contribution amount via the Fees page.
Level Undergraduate Level 2 subject
Assumed Knowledge
Familiarity with computer software programs, such as Microsoft Office.
Assessment
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
| Type | Length | Percent | Threshold | Individual/Group Task |
|---|---|---|---|---|
| Practical: Tutorial Labs -10 marked sessions (2% each) | 2 hours for each session | 20 | N | Individual |
| Applied Project: (Individual) The students are required to develop an effective visualisation for relational data using existing tools or software | 20-25 hours | 30 | N | Individual |
| Applied Project: (Group) The students are required to develop an effective visual analytics work for multi-dimensional data using existing tools or software. | 20-25 hours | 30 | N | Group |
| Intra-session Exam: Closed book, multiple choice | 1 hour | 20 | N | Individual |
Teaching Periods