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  2. Principles of Data Visualisation [DAT3100]

School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

  • Unit Title

    Principles of Data Visualisation

  • Unit Code

    DAT3100

  • Year

    2027

  • Enrolment Period

    1

  • Version

    1

  • Credit Points

    15

  • Full Year Unit

     N

  • Mode of Delivery

    On Campus
    Online

  • Unit Coordinator

    Dr Kat O'MARA

Description

This unit introduces the principles and practices of modern data visualisation. Students will learn how to design, construct, and evaluate visual representations of data to support effective exploration, analysis, and communication. Emphasis is placed on storytelling, critical evaluation, and communication to both technical and non-technical audiences. The unit integrates statistical reasoning, programming, design thinking, communication principles, and computational tools, enabling students to create visualisations that are accurate, ethical, and audience-appropriate. Topics include visual perception, graphical encoding, exploratory data analysis, data storytelling, interactive dashboards, and the use of AI tools to support data exploration and visualisation generation. Students will also critically examine the ethical implications, biases, and limitations of data visualisation approaches.

Prerequisite Rule

Successful completion of Introductory Statistics (MAT1114); Programming Principles (CSP1150); Data Science Fundamentals (DAT2100)

Learning Outcomes

On completion of this unit students should be able to:

  1. Apply principles of human perception and visual design to create clear, effective, and accessible data visualisations.
  2. Select and justify appropriate visual encodings and techniques to explore and analyse data, including identification of patterns, relationships, and insights.
  3. Communicate data-driven insights through well-structured visualisations and narratives tailored to diverse audiences.
  4. Critically evaluate visualisations and AI-assisted outputs, considering effectiveness, bias, ethical implications, and appropriateness.
  5. Develop and implement data visualisations using contemporary tools and advanced techniques, including AI-assisted approaches.

Unit Content

  1. Introduction to data visualisation.
  2. Data types, structures, and visual encoding.
  3. Visual perception, colour, and design principles.
  4. Chart types and selection.
  5. Exploratory data analysis using visualisation (EDA).
  6. Advanced visualisation techniques.
  7. Data storytelling and communication.
  8. Interactive visualisation and dashboards.
  9. AI-assisted visualisation.
  10. Ethics, bias and misrepresentation.

Additional Learning Experience Information

Lectures (face-to-face and/or recorded); tutorials/labs (hands-on coding and visualisation tasks); collaborative and studio-based critique sessions; online activities and discussion forums.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

ON CAMPUS
TypeDescriptionValue
AssignmentIndependent Analysis40%
PortfolioWorkshop Portfolio20%
Project ^Final Project (Report + Presentation)40%
ONLINE
TypeDescriptionValue
AssignmentIndependent Analysis40%
PortfolioWorkshop Portfolio20%
Project ^Final Project (Report + Presentation)40%

^ Mandatory to Pass

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Assessment

Students please note: The marks and grades received by students on assessments may be subject to further moderation. Informal vivas may be conducted as part of an assessment task, where staff require further information to confirm the learning outcomes have been met. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.

Academic Integrity

Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work as well as any generative artificial intelligence tools that may have been used. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.

Breaches of academic integrity can include:

  • Plagiarism: Copying the words, ideas or creative works of other people or generative artificial intelligence tools, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).
  • Unauthorised collaboration (collusion): Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).
  • Contract cheating: Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.
  • Cheating in an exam: Using or having access to unauthorised materials in an exam or test.

Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.

ECU's policies and more information about academic integrity can be found on the student academic integrity website.

All commencing ECU students are required to complete the Academic Integrity Module.

Assessment Extension

In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment Procedure - for more information visit https://askus2.ecu.edu.au/s/article/000001386.

Special Consideration

Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment Procedure - for more information visit https://askus2.ecu.edu.au/s/article/000003318.

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