Outline

Overview

Data can be used to support decision-making, provide insights on a range of topics, and affect change at a personal, business or community level. The visual representation, and communication to an intended audience, plays an important role. The visualisation can make making data useful to a broad audience or target a specific group. This class will cover core principles of data visualisation, to support the creation and evaluation of data visualisations, for audiences ranging from domain experts to novice users. Students will develop an understanding of what makes existing data visualisations work well, create visualisations using industry standard platforms, and evaluate their efficacy.

Unit details and rules

Academic unit	Design Lab
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Phillip Gough, phillip.gough@sydney.edu.au

Assessment

The census date for this unit availability is 8 May 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Visualisation Review Individual, submitted assessment.	30%	Week 06 Due date: 20 May 2026 at 23:59 Closing date: 03 Jun 2026	4 page written report	AI allowed
Outcomes assessed:
Creative work	Visualisation and Evaluation Group activity, creating a data visualisation and user-centred evaluation.	50%	Week 08 Due date: 03 Jun 2026 at 23:59 Closing date: 17 Jun 2026	Interactive Project and Written Report	AI allowed
Outcomes assessed:
Portfolio or journal	Reflective Summary Individual reflection on group activity	20%	Week 08 Due date: 07 Jun 2026 at 23:59 Closing date: 21 Jun 2026	4 page maximum	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

There are 3 assessment items.

All assessments are due on the evening of class.

An individual assessment that involves reviewing existing data visualisations.
- Name: Visualisation Review
- Weight: 30%
- Due: Week 6
An individual assessment that reflects on your involvement in the design process
- Name: Reflective Summary
- Weight: 20%
- Due: Week 8
A group assessment that involves creation and user-centred evaluation of a data visualisation
- Name: Visualisation and Evaluation
- Weight 50%
- Due: Week 8

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)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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	What is Data Visualisation: How We See & Basic Principles	Self-directed learning (4 hr)
Week 01	Data Visualisation Principles	Workshop (1.5 hr)
Week 02	Why Data Visualisation is Backwards: Put the User First, Considering the Audience	Self-directed learning (4 hr)
Week 02	Think Aloud Evaluation	Workshop (1.5 hr)
Week 03	Making data Visualisations: Tools and Platforms	Self-directed learning (4 hr)
Week 03	Visualisation Platform	Workshop (1.5 hr)
Week 04	Communication - The Rule of 3	Self-directed learning (4 hr)
Week 04	Turning Data Into Stories	Workshop (1.5 hr)
Week 05	Making impact in the World: Case Studies on Data Visualisation	Self-directed learning (4 hr)
Week 05	Case Studies and Feedback	Workshop (1.5 hr)
Week 06	From the Analytical to the Artistic: Future of Data Visualisation Technology and Approaches	Self-directed learning (4 hr)
Week 06	Group Project Work and Feedback	Workshop (1.5 hr)

Attendance and class requirements

It is compulsory to attend and participate in all in-person sessions.

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 6 credit point unit, this equates to roughly 120-150 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. Demonstrate an understanding of the principles of data visualisation for a range of audiences
LO2. Critically assess the effectiveness of existing data visualisations against best practice principles
LO3. Apply user-centered design methods to measure effectiveness of a data visualisation’s design
LO4. Use written and interactive visual media to communicate effectively to a range of audiences

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 unit's online discussions have had adjustments based on feedback.

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)

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

ODAT5012: Data Visualisation and Communication

PG Online Session 1B, 2026 [Online] - Online Program

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

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