Course Outline

Download the official university course outline PDF

Course Description

The course offers full introduction to how to design and implement compelling interactive data visualizations. The material is designed for participants to generate meaningful experiences using open source technology to move from an idea through to a finished visualization. The course integrates the design process with highly practical implementation techniques. During this course, participants will learn proven tools, frameworks and concepts useful for creating your own custom data visualization tools rather than relying on off-the-shelf solutions.
Through lectures, in-class workshops & tutorials, and group critiques, students will build their skill sets as data visualization designers while increasing their data and digital literacy. Students will practice the visualization creation process including the following seven steps: Acquire, Parse, Filter, Mine, Represent and Interact. By the end of the course students should have furthered their understanding of essential techniques in making interactive data experiences.
The course should help students appreciate that:

  • All good data visualizations come out of clear design intentions
  • Professionalism and a rigorous UX design process are critical in creating great data visualizations
  • Curiosity, thoughtfulness and a willingness to play (experiment) are the basis of creativity and innovation.

Course Learning Outcomes

In this course students will learn the following:

  • a strong understanding of how to put into practise a highly adopted method of creating Data Visualizations which includes the
    following steps:

    • Acquire Data – Obtain the data, whether from a file on a disk or a source over a network.
    • Parse Data – Provide structure for the data’s meaning, and order it into categories.
    • Filter Data – Remove all but the data of interest.
    • Mine Data – Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context.
    • Represent Data – Choose a basic visual model, such as a bar graph, list, or tree.
    • Refine – Improve the basic representation to make it clearer and more visually engaging.
    • Interact – Add methods for manipulating the data or controlling what features are visible.
  • Attain an understanding of the key techniques and theory used in visualization, including data models, graphical perception and techniques for visual encoding and interaction.
  • Exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, networks, text and cartography.
  • Gain practical experience building and evaluating visualization systems.
  • Acquire the ability to read and discuss research in visualization literature.

Resource Materials

All supported course material and additional resources for the course will be posted and archived at this site:
The course will be using the following text book: