Exploratory Data Analysis and Visualisation G (11517.1)
|Available teaching periods||Delivery mode||Location|
|View teaching periods|| On-Campus
|| UC - Canberra, Bruce
|0.125||3||Faculty Of Science And Technology|
|Discipline||Study level||HECS Bands|
|Academic Program Area - Technology||Graduate Level|| Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
Learning outcomesAfter successful completion of this unit, students will be able to:
1. Choose and apply the most suitable techniques for exploratory data analysis;
2. Map out the hidden underlying structure of the data;
3. Detect anomalies and missing data; and
4. Demonstrate strong skills in using visualisation techniques for analysis and communication.
Graduate attributes1. UC graduates are professional - communicate effectively
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - make creative use of technology in their learning and professional lives
3. UC graduates are lifelong learners - adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas
- 11374 Exploratory Data Analysis and Visualisation
Assumed knowledgeWorking knowledge of discrete mathematics, algebra and numerical analysis.
|Year||Location||Teaching period||Teaching start date||Delivery mode||Unit convener|
|2021||UC - Canberra, Bruce||Semester 1||08 February 2021||On-Campus||Dr Shuangzhe Liu|
|2022||UC - Canberra, Bruce||Semester 1||07 February 2022||On-Campus||Dr Shuangzhe Liu|
There are no prescribed texts for this unit. All necessary learning materials will be available to students via Canvas.
Recommended readings for each week will be provided on the Canvas site.
Submission of assessment items
Special assessment requirements
An aggregate mark of 50% overall as well as a submission of the final project is required to pass the unit.
Your final grade will be determined as follows:
Final mark (100%) = Quiz 1 (20%) + Quiz 2 (30%) + Final Project (50%)
|High Distinction (HD)||85-100|
There will be a total workload of 150 hours which comprises of 24 hours of lectures, 11 hours of labs, 36 hours of review/prep time for quizzes with 4 hours attempt time, and 75 hours of review/prep time and analysis/write-up for the final project.
Your participation in both class and online activities will enhance your understanding of the unit content and therefore the quality of your assessment responses. Lack of participation will result in your inability to satisfactorily pass assessment items.
Required IT skills
This unit assumes some basic knowledge of the statistical programming language R. Some introductory resources will be made available on Canvas. This unit uses the statistical language R for the lab activities and assessments.
Work placement, internships or practicums
Not applicable to this unit.