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 fees via the Fees page.

Level Undergraduate Level 2 subject

Assumed Knowledge

Familiarity with computer software programs, such as Microsoft Office.


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