Welcome to the InfoSci Platform
Reference Hub1
Teaching Visualisation in the Age of Big Data: Adopting Old Approaches to Address New Challenges

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

Belinda A. Chiera, Malgorzata W. Korolkiewicz
Copyright: © 2017 |Pages: 23
ISBN13: 9781522525127|ISBN10: 1522525122|EISBN13: 9781522525134
DOI: 10.4018/978-1-5225-2512-7.ch005
Cite Chapter Cite Chapter

MLA

Chiera, Belinda A., and Malgorzata W. Korolkiewicz. "Teaching Visualisation in the Age of Big Data: Adopting Old Approaches to Address New Challenges." Data Visualization and Statistical Literacy for Open and Big Data, edited by Theodosia Prodromou, IGI Global, 2017, pp. 103-125. https://doi.org/10.4018/978-1-5225-2512-7.ch005

APA

Chiera, B. A. & Korolkiewicz, M. W. (2017). Teaching Visualisation in the Age of Big Data: Adopting Old Approaches to Address New Challenges. In T. Prodromou (Ed.), Data Visualization and Statistical Literacy for Open and Big Data (pp. 103-125). IGI Global. https://doi.org/10.4018/978-1-5225-2512-7.ch005

Chicago

Chiera, Belinda A., and Malgorzata W. Korolkiewicz. "Teaching Visualisation in the Age of Big Data: Adopting Old Approaches to Address New Challenges." In Data Visualization and Statistical Literacy for Open and Big Data, edited by Theodosia Prodromou, 103-125. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2512-7.ch005

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.