Data Visualisation

Undergraduate | 2026

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Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
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Area/Catalogue
MATH X314
Course ID icon
Course ID
208310
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Campus
Adelaide City Campus East, Mawson Lakes
Level of study
Level of study
Undergraduate
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Unit value
6
Course owner
Course owner
Mathematical Sciences
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Course level
1
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

Course overview

The aim of this course is to equip learners with the knowledge and skills necessary to effectively visualise data for the purpose of insightful analysis and impactful communication. Through a combination of theoretical concepts and practical applications, learners will enhance their ability to design clear and compelling visual narratives and leverage various tools and technologies including Python and R to transform raw data into meaningful visual representations. By the end of the course, learners will be able to critically evaluate existing, and create their own, visualisations that facilitate understanding, support decision-making, and engage diverse audiences.

  • Visualisation Construction
  • Visualisation Design
  • Storytelling With Graphics

Course learning outcomes

  • Apply the fundamental concepts and principles of good data visualisation to planning and execution of a data visualisation project
  • Describe and critique common data visualisation approaches to discover insights from data
  • Create data visualisations using data visualisation software including R and Python
  • Assess visualisations for accuracy, clarity, and potential biases, providing constructive feedback and identifying areas for improvement
  • Construct narrative and communicate insights from data effectively through storytelling with visualisations

Prerequisite(s)

  • must have completed 1 of STAT1000 Data Skills for Scientists/STAT5020 Statistical Foundations for Data Science and Artificial Intelligence/STATX100 Probability and Statistics

Corequisite(s)

N/A

Antirequisite(s)

N/A