Exploratory Data Analysis and Visualisation (11374.1)
| Available teaching periods | Delivery mode | Location |
|---|---|---|
| View teaching periods | ||
| EFTSL | Credit points | Faculty |
| 0.125 | 3 | Faculty Of Science And Technology |
| Discipline | Study level | HECS Bands |
| Academic Program Area - Technology | 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. Choose and apply the most suitable techniques for exploratory data analysis;
2. Map out the underlying structure of the data;
3. Detect anomalies and missing data; and
4. Demonstrate competent skills in using visualisation techniques for analysis and communication.
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 G.Equivalent units
None.Assumed knowledge
Working knowledge of discrete mathematics, algebra and numerical analysis.| Year | Location | Teaching period | Teaching start date | Delivery mode | Unit convener |
|---|
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
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%)
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, 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.
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.
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
