DECO3100: Semester 1, 2021

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Units DECO3100 Semester 1 2021 [Normal day]

Unit of study_

DECO3100: Information Visualisation Design Studio

Semester 1, 2021 [Normal day] - Remote

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

Unit code	DECO3100
Academic unit	Design Lab
Credit points	12
Prohibitions ?	None
Prerequisites ?	DECO1016 and DECO2014
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

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

Type	Description	Weight	Due	Length
Assignment	A3: Intelligent Interactive Twitter Visualisation Design and develop an interactive visualisation that tells a story.	40%	STUVAC	56 hours
Outcomes assessed:
Assignment	A1: Infographic Design Design a compelling and effective infographic from the provided dataset.	30%	Week 05	42 hours
Outcomes assessed:
Assignment	A2: Predictive Visualisation Design Design a predictive visualisation from data you prepare yourself.	30%	Week 10	42 hours
Outcomes assessed:

Assessment summary

See Canvas for an in-depth description of all assessible tasks. All assessments for this subject will be due at midnight the day before class.

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 sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Designing with Data	Lecture and tutorial (3 hr)
Week 01	Design Studio	Studio (2 hr)
Week 02	Storytelling with Data	Lecture and tutorial (3 hr)
Week 02	Design Studio	Studio (2 hr)
Week 03	Data-driven Experiences	Lecture and tutorial (3 hr)
Week 03	Design Studio	Studio (2 hr)
Week 04	Time & Relative Dimensions in Space	Lecture and tutorial (3 hr)
Week 04	Design Studio	Studio (2 hr)
Week 05	Data Science for Designers	Lecture and tutorial (3 hr)
Week 05	Design Studio	Studio (2 hr)
Week 06	Correlation, Causality & Data Science Casualties	Lecture and tutorial (3 hr)
Week 06	Design Studio	Studio (2 hr)
Week 07	Artificial Intelligence for Designers	Lecture and tutorial (3 hr)
Week 07	Design Studio	Studio (2 hr)
Week 08	Neural Networks & Deep Learning	Lecture and tutorial (3 hr)
Week 08	Design Studio	Studio (2 hr)
Week 09	Data for Good: Education, Journalism & Accountability	Lecture and tutorial (3 hr)
Week 09	Design Studio	Studio (2 hr)
Week 10	Designing Visual Analytics	Lecture and tutorial (3 hr)
Week 10	Design Studio	Studio (2 hr)
Week 11	Visualising Connections	Lecture and tutorial (3 hr)
Week 11	Design Studio	Studio (2 hr)
Week 12	From Tool to Team-member: Human–AI Collaboration	Lecture and tutorial (3 hr)
Week 12	Design Studio	Studio (2 hr)
Week 13	Entirely Inaccurate Predictions about the Future of AI	Lecture and tutorial (3 hr)
Week 13	Design Studio	Studio (2 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. To design compelling visual narratives using data, including the ability to develop and justify data mappings appropriate for the context.
LO2. To 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. To manipulate, transform and synthesise data into representations that can be used for visualisation, including the ability to recognise and incorporate artificial intelligence principles.
LO4. To 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.

The technical parts of the unit that the students found challenging have been made more detailed and development occurs at a pace suitable to the learning rate of design students.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

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