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COMP5048: Visual Analytics (2015 - Semester 2)

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Unit: COMP5048: Visual Analytics (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Computer Science
Unit Coordinator/s: Professor Hong, SeokHee
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: 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 geometric 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 Visualisaiton 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.
Assumed Knowledge: It is assumed that students will have basic knowledge of data structures, algorithms and programming skills.
Lecturer/s: Professor Hong, SeokHee
Tutor/s: Amyra Meidiana
Timetable: COMP5048 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Tutorial 1.00 1 13
T&L Activities: Tutorial: Tutorial

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Students will need to design and implement a visualisation and analysis method for assignments. Design (Level 4)
Students will learn both fundamental research and latest developments on Graph Drawing and Information Visualisation. Engineering/IT Specialisation (Level 4)
Students are for finding relevant background information for their assignments as well as learing softwares. Information Seeking (Level 4)
Students will need to present a research paper and assignment in the class. Communication (Level 4)
Two assignments are group work. Students will need to organise themselves on effectively completing the task as a team. Project and Team Skills (Level 3)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Design (Level 4)
1. use of geometric algorithms and visualisation methods to solve new problems
2. be able to apply and modify visualisation methods for application area such as social networks and biological networks
Engineering/IT Specialisation (Level 4)
3. knowledge of basic concepts, techniques and algorithms to produce good visualization of abstract data effectively and efficiently
4. understanding of geometric algorithms and visualization methods
5. experience academic research in Graph Drawing and Information Visualisation
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Report No 10.00 Week 9 3, 4,
2 Report Yes 30.00 Week 13 1, 2, 5,
3 Presentation/Seminar No 10.00 Multiple Weeks 3, 4,
4 Participation No 10.00 Multiple Weeks 3, 4,
5 Exam No 40.00 Exam Period 3, 4,
Assessment Description: Report: Assignments 1: Report 1 and Presentation

Report: Assignments 2: Report 2 and Presentation for Programming Assignment

Presentation/Seminar: Paper Presentation 2 (due Week 10-12)

Participation: In class participation

Final Exam
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Minimum Pass Requirement 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. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. 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.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of IT 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.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
Note on Resources:

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Introduction to Visual Analytics
Week 2 Hierarchical Data Visualisation
Week 3 Network Data Visualisation
Week 4 Multivariate/Multidimensional Data Visualisation
Week 5 Dynamic/Temporal Data Visualisation
Week 6 Big Data Visualisation
Week 7 Complex Data Visualisation
Week 8 Evaluation Method
Week 9 Assignment 1 Presentation
Assessment Due: Report
Week 10 Info Vis Paper Presentation
Week 11 Info Vis Paper Presentation
Week 12 Info Vis Paper Presentation.
Week 13 Assignment 2 Presentation
Assessment Due: Report
Exam Period Assessment Due: Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Graduate Diploma in Data Science 2023, 2024, 2025
Master of Complex Systems 2021, 2022, 2023, 2024, 2025
Master of Data Science 2023, 2024, 2025
Master of Data Science (2022 and earlier) 2016, 2017, 2018, 2019, 2020, 2021, 2022
Bachelor of Advanced Computing / Bachelor of Commerce 2018, 2019, 2020, 2021, 2022
Bachelor of Advanced Computing / Bachelor of Science 2018, 2019, 2020, 2021, 2022
Bachelor of Advanced Computing / Bachelor of Science (Health) 2018, 2019, 2020, 2021, 2022
Bachelor of Advanced Computing / Bachelor of Science (Medical Science) 2018, 2019, 2020, 2021, 2022
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computer Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Information Systems) (not offered from 2022+) 2018, 2019, 2020, 2021
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020, 2021, 2022
Bachelor of Computer Science and Technology (Honours) 2015, 2016, 2017, 2025
Biomedical Engineering / Law 2013, 2014
Biomedical Engineering / Arts 2013, 2014
Biomedical Engineering / Commerce 2013, 2014
Biomedical Engineering / Medical Science 2013, 2014
Biomedical Engineering / Science 2013, 2014
Biomedical Engineering (mid-year) 2016, 2017, 2018, 2019, 2020
Biomedical / Project Management 2019+ 2019, 2020
Biomedical Engineering 2016, 2017, 2018, 2019, 2020
Biomedical / Arts (2022 and earlier) 2015, 2016, 2017, 2018, 2019, 2020
Biomedical / Commerce 2015, 2016, 2017, 2018, 2019, 2020
Biomedical /Science 2015, 2016, 2017, 2018, 2019, 2020
Biomedical / Science (Health) 2018, 2019, 2020
Biomedical / Law 2015, 2016, 2017, 2018, 2019, 2020
Software Engineering (mid-year) 2016, 2017, 2018, 2019
Software / Project Management 2019+ 2019
Software Engineering 2015, 2016, 2017, 2018, 2019
Software / Arts (2022 and earlier) 2016, 2017, 2018, 2019
Software / Commerce 2016, 2017, 2018, 2019
Software / Project Management 2016, 2017, 2018
Software / Science 2016, 2017, 2018, 2019
Software / Science (Health) 2018, 2019
Software / Law 2016, 2017, 2018, 2019
Software Engineering / Arts 2011, 2012, 2013, 2014
Software Engineering / Commerce 2010, 2011, 2012, 2013, 2014
Software Engineering / Medical Science 2011, 2012, 2013, 2014
Software Engineering / Science 2011, 2012, 2013, 2014
Biomedical / Science (Medical Science Stream) 2018, 2019, 2020
Graduate Certificate in Digital Health and Data Science 2022, 2023, 2024, 2025
Graduate Diploma in Computing 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Graduate Diploma in Computer Science 2024, 2025
Graduate Diploma in Information Technology 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Graduate Diploma in Complex Systems 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025
Master of Computer Science (advanced entry) (Capstone Pathway) 2024, 2025
Master of Computer Science (advanced entry) (Research Pathway) 2024, 2025
Master of Computer Science (advanced entry) (Work Integrated Pathway) 2024, 2025
Master of Computer Science (Capstone Pathway) 2024, 2025
Master of Computer Science (Research Pathway) 2024, 2025
Master of Computer Science (Work Integrated Pathway) 2024, 2025
Master of Digital Health and Data Science 2022, 2023, 2024, 2025
Master of Health Technology Innovation 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022
Master of Information Technology 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Master of Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Master of IT / Master of IT Management 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023
Software / Science (Medical Science Stream) 2018, 2019

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
Maths/Science Methods and Tools (Level 4) No 0%
Design (Level 4) Yes 24%
Engineering/IT Specialisation (Level 4) Yes 76%
Information Seeking (Level 4) Yes 0%
Communication (Level 4) Yes 0%
Professional Conduct (Level 3) No 0%
Project and Team Skills (Level 3) Yes 0%

These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.