Teaching Visualisation in the Age of Big Data: Adopting Old Approaches to Address New Challenges

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Author(s): Belinda A. Chiera (University of South Australia, Australia)and Malgorzata W. Korolkiewicz (University of South Australia, Australia)
Copyright: 2017
Pages: 23
Source title: Data Visualization and Statistical Literacy for Open and Big Data
Source Author(s)/Editor(s): Theodosia Prodromou (University of New England, Australia)
DOI: 10.4018/978-1-5225-2512-7.ch005

Keywords: Data Analysis & Statistics / Information Retrieval / Information Science Reference / Library & Information Science

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Abstract

Technological advances have led to increasingly more data becoming available, a phenomenon known as Big Data. The volume of Big Data is to the order of zettabytes, offering the promise of valuable insights with visualisation the key to unlocking these insights, however the size and variety of Big Data poses significant challenges. The fundamental principles behind tried-and-tested methods for visualising data are still as relevant as ever, although the emphasis necessarily shifts to why visualisation is being attempted. This chapter outlines the use of graph semiotics to build data visualisations for exploration and decision-making and the formulation of elementary, intermediate- and overall-level analytical questions. The public scanner database Dominick's Finer Foods, consisting of approximately 98 million observations, is used as a demonstrative case study. Common Big Data analytic tools (SAS, R and Python) are used to produce visualisations and exemplars of student work are presented, based on the outlined visualisation approach.

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