COMP5048: Visual Analytics (2019 - 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 Ereina Gomez Shijun Cai Supraja Sridharan |
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| Timetable: | COMP5048 Timetable | |||||||||||||||
| Time Commitment: |
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| 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: |
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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 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.
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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 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 Goals
This unit contributes to the achievement of the following course goals:
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.
