Exploratory Data Analysis and Visualisation (11374.1)

Please note these are the 2022 details for this unit
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Exploratory Data Analysis and Visualisation
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
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)
Data and its analysis and modelling underpin all aspects of work and society in the 21st century. This unit provides students with a thorough study exploratory data analysis and visualisation techniques. Exploratory Data Analysis is an approach to data analysis that employs a variety of techniques, mostly graphical. The main role of this approach is to open-mindedly explore the data. Visualisation enables the data to reveal its structural secrets and provide new insight into the data. Exploratory Data Analysis allows the data scientist to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations. This unit will provide hands-on experience in data visualisation and summary statistics using real-world data examples.
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 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

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.
Availability for enrolment in 2024 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
2023 Bruce, Canberra Semester 1 06 February 2023 On-Campus Dr Shuangzhe Liu
2024 Bruce, Canberra Semester 1 05 February 2024 On-Campus Dr Shuangzhe Liu
Not available

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%)

Academic integrity

Students have a responsibility to uphold University standards on ethical scholarship. Good scholarship involves building on the work of others and use of others' work must be acknowledged with proper attribution made. Cheating, plagiarism, and falsification of data are dishonest practices that contravene academic values. Refer to the University's Student Charter for more information.

To enhance understanding of academic integrity, all students are expected to complete the Academic Integrity Module (AIM) at least once during their course of study. You can access this module within UCLearn (Canvas) through the 'Academic Integrity and Avoiding Plagiarism' link in the Study Help site.

Use of Text-Matching Software

The University of Canberra uses text-matching software to help students and staff reduce plagiarism and improve understanding of academic integrity. The software matches submitted text in student assignments against material from various sources: the internet, published books and journals, and previously submitted student texts.

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