COMP5048: Visual Analytics (2019 - 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 using Information Visualisation.

Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed visualisations 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 applications (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making.

This unit will provide visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information,

as well as basic HCI concepts. Furthermore, 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: Niku Gorji (TA)

Amyra Meidiana (TA)

Marnijati Torkel

Ereina Gomez

Shijun Cai

Supraja Sridharan
Timetable: COMP5048 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Workshop 1.00 1 8
T&L Activities: Workshop

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.

Unassigned Outcomes
1. Be able to select appropriate visual variables, space utilisation methods and levels of organisation of visual components to depict complex data
2. Be able to select, apply and modify visualisation methods suited to a given problem domain in order to facilitate data analytic process through visual inspection.
3. Understanding of basic computational concepts, techniques and algorithms to produce good visualization of abstract data
4. Understanding of the basic Human-Computer Interaction principles, which influence the production of good/effective visualisation
5. Experience academic research in Data Visualisation/ Visual Analytics
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Assignment 1 No 10.00 Week 7 1, 2,
2 Assignment 2: Final Report Yes 15.00 Week 13 1, 2,
3 Assignment 2: Presentation Yes 10.00 Week 10 1, 2, 3, 4,
4 Homework No 10.00 Multiple Weeks 1, 2, 3, 4,
5 Final Exam No 50.00 Exam Period 1, 2, 3, 4,
6 Assignment 2: Initial Report Yes 5.00 Week 8 1, 2, 3,
Assessment Description: Homework: Week 2-9

Assignment 1: Week 7

Assignment 2:

1. Initial Report: Week 8

2. Presentation: Week 10 (10-12)

3. Final Report: Week 13

Exam (Exam Period)
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 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.

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 Data Types and Visual Representation
Week 3 Relational Visualisation I
Week 4 Relational Visualisation II
Week 5 Big Data Visualisation
Week 6 Complex Data Visualisation
Week 7 Human Visual System, Perception, Color
Assessment Due: Assignment 1
Week 8 Visual Analytic System Example
Assessment Due: Assignment 2: Initial Report
Week 9 Evaluation Methods
Week 10 Visual Analytic System Presentation
Assessment Due: Assignment 2: Presentation
Week 11 Visual Analytic System Presentation
Assessment Due: Assignment 2: Presentation
Week 12 Assessment Due: Assignment 2: Presentation
Visual Analytic System Presentation.
Week 13 Review
Assessment Due: Assignment 2: Final Report
Exam Period Assessment Due: Final Exam

Course Relations

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

Course Year(s) Offered
Master of Data Science 2016, 2017, 2018, 2019
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019
Bachelor of Advanced Computing (Software Development) 2018, 2019
Bachelor of Computer Science and Technology (Honours) 2015, 2016, 2017
Bachelor of Computer Science and Technology (Honours) 2014 2013, 2014
Biomedical Engineering / Law 2013, 2014
Biomedical Engineering / Arts 2013, 2014
Biomedical Engineering / Commerce 2013, 2014
Biomedical Engineering / Medical Science 2013, 2014
Biomedical Engineering / Project Management 2013, 2014
Biomedical Engineering / Science 2013, 2014
Biomedical - Chemical and Biomolecular Major 2013, 2014, 2015
Biomedical - Electrical Major 2013, 2014
Biomedical - Information Technology Major 2013, 2014, 2015
Biomedical - Mechanical Major 2013, 2014, 2015
Biomedical - Mechatronics Major 2013, 2014, 2015
Biomedical Mid-Year 2016, 2017, 2018, 2019
Biomedical/ Project Management 2019
Biomedical 2016, 2017, 2018, 2019
Biomedical / Arts 2015, 2016, 2017, 2018, 2019
Biomedical / Commerce 2015, 2016, 2017, 2018, 2019
Biomedical / Medical Science 2015, 2016, 2017
Biomedical / Music Studies 2016, 2017
Biomedical / Project Management 2015, 2016, 2017, 2018
Biomedical /Science 2015, 2016, 2017, 2018, 2019
Biomedical/Science (Health) 2018, 2019
Biomedical - Electrical Major 2015
Biomedical / Law 2015, 2016, 2017, 2018, 2019
Software Mid-Year 2016, 2017, 2018, 2019
Software/ Project Management 2019
Software 2015, 2016, 2017, 2018, 2019
Software / Arts 2016, 2017, 2018, 2019
Software / Commerce 2016, 2017, 2018, 2019
Software / Medical Science 2016, 2017
Software / Music Studies 2016, 2017
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 (till 2014) 2010, 2011, 2012, 2013, 2014
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 / Project Management 2012, 2013, 2014
Software Engineering / Science 2011, 2012, 2013, 2014
Biomedical/Science (Medical Science Stream) 2018, 2019
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Bachelor of Information Technology (Computer Science) 2014 and earlier 2009, 2010, 2011, 2012, 2013, 2014
Information Technology (Computer Science)/Arts 2012, 2013, 2014
Information Technology (Computer Science) / Commerce 2012, 2013, 2014
Information Technology (Computer Science) / Medical Science 2012, 2013, 2014
Information Technology (Computer Science) / Science 2012, 2013, 2014
Information Technology (Computer Science) / Law 2012, 2013, 2014
Bachelor of Information Technology (Information Systems) 2014 and earlier 2010, 2011, 2012, 2013, 2014
Information Technology (Information Systems)/Arts 2012, 2013, 2014
Information Technology (Information Systems) / Commerce 2012, 2013, 2014
Information Technology (Information Systems) / Medical Science 2012, 2013, 2014
Information Technology (Information Systems) / Science 2012, 2013, 2014
Information Technology (Information Systems) / Law 2012, 2013, 2014
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017
Graduate Certificate in Information Technology 2015, 2016, 2017, 2018, 2019
Graduate Certificate in Information Technology Management 2015, 2016, 2017, 2018, 2019
Graduate Diploma in Computing 2015, 2016, 2017, 2018, 2019
Graduate Diploma in Information Technology 2015, 2016, 2017, 2018, 2019
Graduate Diploma in Information Technology Management 2015, 2016, 2017, 2018, 2019
Graduate Certificate in Information Technology (till 2014) 2012, 2013, 2014
Graduate Diploma in Complex Systems 2017, 2018, 2019
Graduate Diploma in Information Technology (till 2014) 2012, 2013, 2014
Master of Complex Systems 2017, 2018, 2019
Master of Health Technology Innovation 2015, 2016, 2017, 2018, 2019
Master of Information Technology 2015, 2016, 2017, 2018, 2019
Master of Information Technology Management 2015, 2016, 2017, 2018, 2019
Master of IT/Master of IT Management 2015, 2016, 2017, 2018, 2019
Master of Information Technology (till 2014) 2014
Software/Science (Medical Science Stream) 2018, 2019

Course Goals

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

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 4) No 0%
(7) Project and Team Skills (Level 3) No 0%
(8) Professional Effectiveness and Ethical Conduct (Level 3) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(4) Design (Level 4) No 0%
(2) Engineering/ IT Specialisation (Level 4) No 0%
(3) Problem Solving and Inventiveness (Level 4) No 0%
(1) Maths/ Science Methods and Tools (Level 4) No 0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 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.