New version of FIT2079, shifted towards 3rd year level to mesh with redeveloped BCS / Data Science stream course architecture redesign.

Introduced for course architecture programs. Effective semester 1, 2018

21/09/2016: Updated to keep inline with FIT2079.

12/06/2017: Admin - updating location of offering to reflect actual campus offerings at the ADE's request.

24/9/19: Admin - updating the exam duration to include additional 10 minutes as per University requirement.

07/07/2020: Removed exam assessment for S2-2020.

07/10/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

Core unit in the BCS Data Science stream.

At the completion of this unit, students should be able to:

1. critically analyse data visualisations;
2. create effective data visualisations;
3. describe the main applications of data visualisation in business, engineering and design, and the social and physical sciences;
4. describe the advantages, drawbacks and pitfalls of the visual presentation of data as compared to its presentation using other media.

029999

Data visualisation is a powerful technique that allows us to use our visual system to understand data. Interactive data visualisation is now common in business, engineering and design and the social and physical sciences. This unit introduces the main kinds of information graphics and interactive visualisation systems and their areas of application. It investigates the reasons why visualisation can be effective and based on this students will gain experience in critically assessing data visualisations and in designing their own visualisations. Students will learn how to create visualisations with representative computer tools and gain experience in creating a data visualisation for an application domain of their choice.

On-campus

Lecture and tutorials or problem classes

Students are interviewed on their efforts in both visualisation assignments.

1. Tutorial Tasks 15% - Learning outcomes: 1, 3, 4
2. Class Tests 35% - Learning outcomes: 1, 3, 4
3. Data Visualisation I 25% - Learning outcomes: 2
4. Data Visualisation II 25% - Learning outcomes: 2

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

(b.) Additional requirements (all students):

The last week involves a visit to the CAVE 2 facility to be scheduled.

R package, Shiny, D3 (Javascript library).

Completion of 24 points at first year.