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Queensland University of Technology

Big Data: Data Visualisation

Queensland University of Technology via FutureLearn

This course may be unavailable.


Data visualisation is an important visual method for effective communication and analysing large datasets. Through data visualisations we are able to draw conclusions from data that are sometimes not immediately obvious and interact with the data in an entirely different way.

This course will provide you with an informative introduction to the methods, tools and processes involved in visualising big data. We will also take the time to examine briefly the use of visualisation throughout history dating back as far as 17000 BC.

We have designed the course for people from different fields who want to learn how to produce visualisations that help us better understand real-world big data problems. You will gain the most from the practical exercises if you are comfortable with computer programming however you don’t need to have any prior experience using the software listed below.

We will use a variety of tools so that you become comfortable engaging with different software and confident trialing new packages to find those that best meet your needs. Please review the product websites below to ensure your system meets the minimum requirements for the tools we will be using.

  • Tableau: You can use the free trial for a period of 2 weeks. Please do not start the trial until you are ready to do the Tableau exercises.
  • MATLAB Online: MathWorks will provide you with a license to use MATLAB online for the duration of the course.
  • D3.js: The D3 JavaScript library is available under BSD license.

You can still learn effectively even if you don’t have access to all of these tools as you will be able to see what they can do for you.

Taught by

Tomasz Bednarz, Ian Turner and Kerrie Mengersen


4.0 rating, based on 1 Class Central review

Start your review of Big Data: Data Visualisation

  • This short course gives you good opportunities to explore many data visualisations techniques and tools.
    Good as introduction to get a feeling on visualisations in case you don't know yet where to start. Once you decided on a technology / framework you will need more learning and practice to become proficient.
    The more time you spend on the course the more you will learn, the 2 hrs / week mentioned on the course info page is the absolute minimum, please do take more time, eg to interact with other students, 4 hrs / week looks like a better figure to me.

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