Exploratory Data Analysis and Visualisation (11374.2)
| Available teaching periods | Delivery mode | Location |
|---|---|---|
| View teaching periods | On-campus |
Bruce, Canberra |
| EFTSL | Credit points | Faculty |
| 0.125 | 3 | Faculty Of Science And Technology |
| Discipline | Study level | HECS Bands |
| School Of Information Technology & Systems | Level 3 - Undergraduate Advanced Unit | Band 2 2021 (Commenced After 1 Jan 2021) Band 3 2021 (Commenced Before 1 Jan 2021) |
This unit may be cotaught with 11517 Exploratory Data Analysis and Visualisation G.
Learning outcomes
After successful completion of this unit, students will be able to:1. Detect missing values, outliers and other abnormal data prior to exploratory data analysis;
2. Identify the hidden underlying structures and patterns of the variables within the data;
3. Examine ideas and methods used in exploratory data analysis for real world applications; and
4. Demonstrate competent skills in using data visualisation techniques for analysis and communication of findings and results, along with statistical reporting.
Graduate attributes
1. UC graduates are professional - communicate effectively1. 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
3. UC graduates are lifelong learners - evaluate and adopt new technology
Prerequisites
Must have passed 24 credit points.Corequisites
None.Incompatible units
11517 Exploratory Data Analysis and Visualisation GEquivalent units
None.Assumed knowledge
Working knowledge of discrete mathematics, algebra and numerical analysis.| Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
|---|---|---|---|---|---|
| 2026 | Bruce, Canberra | Semester 1 | 16 February 2026 | On-campus | Dr Shuangzhe Liu |
| 2027 | Bruce, Canberra | Semester 1 | 15 February 2027 | On-campus | Dr Shuangzhe Liu |
Required texts
There are no prescribed texts for this unit. All necessary learning materials will be available to students via Canvas.
A recommended text is Camm et al. (2021) Data Visualization Exploring and Explaining with Data Cengage, which is also available at the UC library. Other recommended readings will be provided on the Canvas site.
Submission of assessment items
Extensions & Late submissions
Artificial Intelligence (AI)
Guided - Students will be guided in how GenAI must/may be used in completing the assessments as detailed in the unit outline and assessment instructions. More detailed information can be found at GenAI and Assessment at UC.
Please refer to each assessment item description on the unit's teaching site to determine where the use of AI and GenAI is permitted (and how so) and where it is prohibited. Any use of AI and GenAI must be appropriately acknowledge and referenced and in accordance with what has been permitted in the assessment description. Please refer to the Referencing GenAI guide and full assessment details in the unit's Canvas teaching site.
Special assessment requirements
An aggregate mark of 50% overall is required to pass the unit.
Your final grade will be determined as follows:
Final mark (100%) = Quiz 1 (10%) + Quiz 2 (15%) + Quiz 3 (15%) + Case Study (60%)
The unit convenor reserves the right to question students on any of their submitted work for moderation and academic integrity purposes.
Students must apply academic integrity in their learning and research activities at UC. This includes submitting authentic and original work for assessments and properly acknowledging any sources used.
Academic integrity involves the ethical, honest and responsible use, creation and sharing of information. It is critical to the quality of higher education. Our academic integrity values are honesty, trust, fairness, respect, responsibility and courage.
UC students have to complete the Academic Integrity Module annually to learn about academic integrity and to understand the consequences of academic integrity breaches (or academic misconduct).
UC uses various strategies and systems, including detection software, to identify potential breaches of academic integrity. Suspected breaches may be investigated, and action can be taken when misconduct is found to have occurred.
Information is provided in the Academic Integrity Policy, Academic Integrity Procedure, and University of Canberra (Student Conduct) Rules 2023. For further advice, visit Study Skills.
Learner engagement
There will be a total workload of 150 hours which comprises of 24 hours of lectures, 11 hours of labs, 34 hours of review/prep time for quizzes with 6 hours attempt time, and 75 hours of review/prep time and analysis/write-up for the case study.
Participation requirements
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.
Attendance at the Week 5 and the Week 12 labs is mandatory in order to successfully complete the in-class quiz (Week 5) and the case study presentation (Week 12).
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
