Outline

Overview

The field of information visualisation focuses on how data can be effectively represented and meaningfully communicated to people, in interactive and automated ways. The unit of study introduces the principles of information visualisation design, with special attention to aesthetic communication of data, data analytics, and user engagement. Key concepts covered in this unit include: abstract data visualisation; data acquisition; and parsing and processing of data. Using a combination of vector graphics software tools and programming languages for processing data, students will develop information visualisations of real-world datasets that are both communicative and engaging. The unit will equip students with the skills to produce static as well as web-ready interactive data visualisations.

Unit details and rules

Academic unit	Design Lab
Credit points	12
Prerequisites ?	DECO1016 and DECO2014
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Kazjon Grace, kazjon.grace@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Presentation	A1: Visualisation Concept Presentation Presenting progress on the major project, concept + feasibility.	30%	- Due date: 27 Apr 2025 at 23:59	42hrs (approximate expected work)
Outcomes assessed:
Assignment	A2: Interactive Visualisation Project Design and develop an interactive visualisation that tells a story.	50%	- Due date: 01 Jun 2025 at 23:59	70hrs (approximate expected work)
Outcomes assessed:
Attendance	Tutorial & Studio Attendance Students are required to meet the minimum 90% attendance to pass this unit.	0%	Multiple weeks	Duration of class.
Outcomes assessed:
Small continuous assessment	Marked Tutorial Task 1 Expanded tutorial exercise designed to be completed in studio time.	4%	Week 02 Due date: 03 Mar 2025 at 23:59	~3hrs.
Outcomes assessed:
Small continuous assessment	Marked Tutorial Task 2 Expanded tutorial exercise designed to be completed in studio time.	4%	Week 03 Due date: 10 Mar 2025 at 23:59	~3hrs.
Outcomes assessed:
Small continuous assessment	Marked Tutorial Task 3 Expanded tutorial exercise designed to be completed in studio time.	4%	Week 04 Due date: 17 Mar 2025 at 23:59	~3hrs.
Outcomes assessed:
Small continuous assessment	Marked Tutorial Task 4 Expanded tutorial exercise designed to be completed in studio time.	4%	Week 05 Due date: 24 Mar 2025 at 23:59	~3hrs.
Outcomes assessed:
Small continuous assessment	Marked Tutorial Task 5 Expanded tutorial exercise designed to be completed in studio time.	4%	Week 06 Due date: 31 Mar 2025 at 23:59	~3hrs.
Outcomes assessed:
= hurdle task ? = AI allowed ?

Assessment summary

See Canvas for an in-depth description of all assessible tasks.

Attendance: Students should be present and engaged in their learning during classes. Late arrival/early departure will be deemed as an absence. Students who do not meet the minimum 90% threshold, who have approved special consideration may be offered the opportunity to sit an alternative assessment to pass this unit.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.
Distinction	75 - 84	Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.
Credit	65 - 74	Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.
Pass	50 - 64	Work demonstrating satisfactory achievement of the learning outcomes assessed.
Fail	0 - 49	Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Designing with Data	Lecture (1 hr)
	Charts	Tutorial (2 hr)
	Chart Critique	Studio (3 hr)
Week 02	Storytelling with Data	Lecture (1 hr)
	Manipulating Data	Tutorial (2 hr)
	Tutorial Task 1	Studio (3 hr)
Week 03	Data Science for Designers	Lecture (1 hr)
	Web Technologies	Tutorial (2 hr)
	Tutorial Task 2	Studio (3 hr)
Week 04	Evaluating Visualisations	Lecture (1 hr)
	Intermediate Charts	Tutorial (2 hr)
	Tutorial Task 3	Studio (3 hr)
Week 05	Time & Relative Dimensions in Space	Lecture (1 hr)
	Data Preparation	Tutorial (2 hr)
	Tutorial Task 4	Studio (3 hr)
Week 06	Visualisation in Practice	Lecture (1 hr)
	Spatial Data	Tutorial (2 hr)
	Tutorial Task 5	Studio (3 hr)
Week 07	Project Brief	Lecture (1 hr)
	Animated Charts	Tutorial (2 hr)
	Studio Work	Studio (3 hr)
Week 08	Designing Analytics: Interacting with Visualisations	Lecture (1 hr)
	Interactive Charts	Tutorial (2 hr)
	Studio Work	Studio (3 hr)
Week 09	Project Q&A	Tutorial (2 hr)
Week 09	Presentations	Studio (3 hr)
Week 10	Visualisation Design in Practice	Lecture (1 hr)
	Advanced Charting	Tutorial (2 hr)
	Studio Work	Studio (3 hr)
Week 11	Visualising Connections	Lecture (1 hr)
	Networks	Tutorial (2 hr)
	Studio Work	Studio (3 hr)
Week 12	Artificial Intelligence for Designers	Lecture (1 hr)
	AI Topics	Tutorial (2 hr)
	Studio Work	Studio (3 hr)
Week 13	Entirely Inaccurate Predictions About The Future of AI	Lecture (1 hr)
	Project Q&A	Tutorial (2 hr)
	Studio Work	Studio (3 hr)

Attendance and class requirements

Please refer to the Resolutions of the University School: http://sydney.edu.au/handbooks/architecture/rules/faculty_resolutions.shtml

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 12 credit point unit, this equates to roughly 240-300 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

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

LO1. design compelling visual narratives using data, including the ability to develop and justify data mappings appropriate for the context.
LO2. recognise, demonstrate and implement aesthetic and human-centred design qualities, including the ability to devise and justify an appropriate design solutions based on a brief.
LO3. manipulate, transform and synthesise data into representations that can be used for visualisation, including the ability to recognise and incorporate artificial intelligence principles.
LO4. hypothesise, evaluate and revise data visualisations based on rigorous qualitative and quantitative user-centred evaluation.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This year we're expanding our permissions on use of LLMs in tutorials and the assignment. This unit is about telling insightful stories with data, and we think that's not (yet) something an AI can do on its own, so we're encouraging students to use LLMs to help them prepare data and build charts.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

DECO3100: Information Visualisation Design Studio

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links