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Unit of study_

QBUS6860: Visual Data Analytics

Accurate and effective analysis of data is a crucial skill in today's data-rich business environment. Visual Data Analytics (VDA) is an indispensable scientific tool for analysing all sorts of business-related data and, in particular, complex high-dimensional data. Applications include the visualisation of financial statements, capital market data, marketing data, supply chain data and many others. VDA has the ability to encode vast amounts of information into a small space that can be then intuitively interpreted for decision-making. This unit draws upon statistics, computer science, behavioural psychology and information design for visualising numerical and text data. It presents statistical and data analysis methods that are necessary for description, exploration, inference and diagnosis using data reduction, visual mining, smoothing, clustering and validation techniques. Upon completion of the unit, students should be proficient in producing high integrity visuals that enable fast and precise business decision-making. Students will also learn about the limitations of visual perception and how to design powerful visuals that can tap into our natural cognitive predisposition in favouring visual types of information.

Code QBUS6860
Academic unit Business Analytics
Credit points 6
Prerequisites:
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QBUS5001 or QBUS5002
Corequisites:
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None
Prohibitions:
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None
Assumed knowledge:
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The unit assumes knowledge of statistics and confidence in working with data

At the completion of this unit, you should be able to:

  • LO1. Explore information using graphical methods.
  • LO2. Match available data to the most appropriate visualisations to assist in problem solving.
  • LO3. Use visualisation appropriately and effectively to support business decision making and business problem solving.
  • LO4. Communicate your visual analytics results and explain your findings to a business audience.
  • LO5. Critically evaluate visualisation methods, through individual and stimulating work with peers.
  • LO6. Identify potential biases, which visualisations may generate, using developed experience in ethical and socially responsible visual analytics.