Exploratory Data Analysis and Visualisation G (11517.2)

Please note these are the 2027 details for this unit
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Exploratory Data Analysis and Visualisation G
Available teaching periods Delivery mode Location
View teaching periods On-campus
Online self-paced
 Bruce, Canberra
EFTSL Credit points Faculty
0.125 3 Faculty Of Science And Technology
Discipline Study level HECS Bands
School Of Information Technology & Systems Graduate Level Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
This unit equips students with essential skills in exploratory data analysis and data visualisation, emphasising their importance in modern work and society. Students will learn data wrangling techniques for preparing datasets, handling missing values, and managing outliers. They will explore graphical methods for analysing univariate, bivariate, and multivariate data, uncovering patterns, spotting anomalies, testing hypotheses, and validating assumptions using summary statistics and visualisations. By the end of the unit, students will have gained practical experience in applying relevant techniques to real-world data, enhancing their ability to derive insights and make data-driven decisions.

This unit may be co-taught with 11374 Exploratory Data Analysis and Visualisation.

Learning outcomes

After successful completion of this unit, students will be able to:
1. Critically analyse and interpret missing values, outliers and other abnormal data prior to exploratory data analysis;
2. Critically analyse the variables within the data to identify their hidden underlying structures and patterns;
3. Examine ideas and methods used in exploratory data analysis for real world applications; and
4. Use data visualisation techniques in the analysis and communication of statistical reports, findings and results.


Graduate attributes

1. 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
3. UC graduates are lifelong learners - evaluate and adopt new technology


Prerequisites

None.

Corequisites

None.

Incompatible units

11374 Exploratory Data Analysis and Visualisation

Equivalent units

None.

Assumed knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.
Availability for enrolment in 2027 is subject to change and may not be confirmed until closer to the teaching start date.
Year Location Teaching period Teaching start date Delivery mode Unit convener
2026 Bruce, Canberra Semester 1 16 February 2026 On-campus Dr Shuangzhe Liu
2026 Bruce, Canberra Semester 1 16 February 2026 Online self-paced Dr Shuangzhe Liu
2026 Bruce, Canberra Semester 2 10 August 2026 Online self-paced Dr Shuangzhe Liu
2027 Bruce, Canberra Semester 1 15 February 2027 On-campus Dr Shuangzhe Liu
2027 Bruce, Canberra Semester 1 15 February 2027 Online self-paced Dr Shuangzhe Liu
2027 Bruce, Canberra Semester 2 09 August 2027 Online self-paced Dr Shuangzhe Liu
The information provided should be used as a guide only. Timetables may not be finalised until week 2 of the teaching period and are subject to change. Search for the unit timetable.

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

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