Note: This unit is an archived version! See Overview tab for delivered versions.
COMP5048: Visual Analytics (2018 - Semester 2)
| 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
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| 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
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| Tutor/s: |
Niku Gorji (TA) Amyra Meidiana (TA) Marnijati Torkel Jingming Hu Jialu Chen Anirudh Sharma Supraja Sridharan |
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| Timetable: | COMP5048 Timetable | |||||||||||||||
| Time Commitment: |
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| 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) |
| Some 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 2018.
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)| Assessment Methods: |
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| 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) |
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| Grading: |
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| 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.
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| 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 |
| Week 2 | Visualisation of Complex Data |
| Week 3 | Visualisation of Relational Data I |
| Week 4 | Visualisation of Relational Data II |
| Week 5 | Visualisation of Big Data |
| Week 6 | Visualisation of Dynamic Data |
| Week 7 | Visual Analytic System |
| Assessment Due: Assignment 1 | |
| Week 8 | Human Perception, Color |
| 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 | Visual Analytic System Presentation. |
| Assessment Due: Assignment 2: 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 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 | 51% |
| Engineering/IT Specialisation (Level 4) | Yes | 49% |
| 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 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.
