COMP 2026 Visual Analytics

Credit Points 10

Legacy Code 301489

Coordinator Mahsa Razavi 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 Computer Science

Student Contribution Band HECS Band 2 10cp

Check your HECS Band contribution amount via the Fees page.

Level Undergraduate Level 2 subject

Restrictions

Students enrolled in programs other than the 3769 Bachelor of Data Science or 3770 Bachelor of Applied Data Science must have successfully completed 60 credit points.

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 2 hours for each session 20 N Individual
Report 20-25 hours 30 N Individual
Report 20-25 hours 30 N Group
Multiple Choice 1 hour 20 N Individual

Teaching Periods

Autumn (2022)

Parramatta - Victoria Rd

Day

Subject Contact Quang Vinh Nguyen Opens in new window

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Sydney City Campus - Term 2 (2022)

Sydney City

Day

Subject Contact Mahsa Razavi Opens in new window

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Autumn (2023)

Parramatta - Victoria Rd

On-site

Subject Contact Mahsa Razavi Opens in new window

View timetable Opens in new window

Sydney City Campus - Term 2 (2023)

Sydney City

On-site

Subject Contact Mahsa Razavi Opens in new window

View timetable Opens in new window