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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

Unit of study_

COMP5048: Visual Analytics

Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.


Academic unit Computer Science
Unit code COMP5048
Unit name Visual Analytics
Session, year
Semester 2, 2020
Attendance mode Normal evening
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Assumed knowledge

Experience with data structures and algorithms as covered in COMP9103 OR COMP2123 OR COMP2823 OR INFO1105 OR INFO1905 (or equivalent UoS from different institutions).

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Seokhee Hong,
Lecturer(s) Seokhee Hong ,
Tutor(s) Mike Li ,
Amyra Meidiana,
Martina Tian,
Marni Torkel,
Shijun Cai,
Administrative staff Amyra Meidiana (TA) Marni Torkel (TA)
Type Description Weight Due Length
Final exam (Review+) Type B final exam Final exam
Exam Type B
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Homework
10% Multiple weeks n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment 1
15% Week 07 n/a
Outcomes assessed: LO1 LO2 LO3
Presentation group assignment Assignment 2: Presentation
10% Week 09 n/a
Outcomes assessed: LO1 LO2
Assignment group assignment Assignment 2: Final report
15% Week 12 n/a
Outcomes assessed: LO1 LO2
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Detailed information for each assessment can be found on Canvas

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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. A student must also achieve an overall final mark of 50 or more.

Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see

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:

The Assessment Procedures 2011 provide that any work submitted after the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Introduction to visual analytics Lecture (2 hr) LO3
Week 02 Data types and visual representation Lecture (2 hr) LO1 LO3
Week 03 Relational visualisation 1 Lecture (2 hr) LO3
Week 04 Relational visualisation 2 Lecture (2 hr) LO3
Week 05 Big data visualisation Lecture (2 hr) LO2
Week 06 Complex data visualisation Lecture (2 hr) LO1 LO3
Week 07 Human visual system and Perception; Colour Lecture (2 hr) LO4
Week 08 Evaluation Lecture (2 hr) LO4
Week 09 VA System Presentation I Presentation (2 hr) LO2
Week 10 VA System Presentation II Presentation (2 hr) LO2
Week 11 VA System Presentation III Presentation (2 hr) LO2
Week 12 Review Lecture (2 hr)  

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.

Prescribed readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

  • Giuseppe Di Battista, Peter Eades, Roberto Tamassia, Ioannis G. Tollis, Graph Drawing: Algorithms for the Visualizaiton of Graphs. Prentice-Hall, 1999.
  • Colin Ware, Information Visualization : Perception for Design (2nd). Elsevier, 2004.
  • Stuart Card, Jock Mackinlay, and Ben Shneiderman, Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, 1999.
  • Tamara Munzner, Visualization Analysis and Design. 2014.

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.

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

  • LO1. select appropriate visual variables, space utilisation methods, and levels of organisation of visual components, to depict complex data
  • LO2. select, apply, and modify visualisation methods suited to a given problem domain, in order to facilitate data analytic process through visual inspection
  • LO3. understand basic computational concepts, techniques, and algorithms to produce good visualisation of abstract data
  • LO4. understand the basic human-computer interaction principles, which influence the production of good/effective visualisation

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
Some assesment has been changed for covid19 teaching.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism. 

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see, or the similarity report available in ED ( These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.”

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.


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.