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What can be done with the data, once it has been collected? Two suggestions come to mind: analysis is the process by which we extract actionable insights from the data (this process is discussed in later posts), while visualisation is the process of presenting data, calculations, and analysis outputs in a visual format. Visualisation of data prior to analysis can help simplify the analytical process. Visualisation following analysis allows for the analysis results to be presented to various stakeholders

In this post, we focus on important visualisation concepts and methods; we shall provide examples of data displays to illustrate the various possibilities that might be produced by the data presentation component of a data analysis system.

Data Science Report Series #8: Data Visualisation and Representation (Draft), by Patrick Boily, Jennifer Schellinck, and Shintaro Hagiwara.

Post Author: Patrick Boily

Patrick is a graduate from the University of Ottawa. He obtained his Ph.D. in Mathematics in 2006. He has taught over 35 courses at Universities in the Ottawa area since 1999, and worked on a number of projects as a federal public servant from 2008 to 2012. He joined Carleton University in 2012 to start and manage the Centre for Quantitative Analysis and Decision Support (CQADS), and is an Adjunct Professor at both the University of Ottawa and Carleton University. He is the president of Idlewyld Analytics and Consulting Services since 2016.

Patrick’s academic interests reside in the application of mathematics and statistics to evidence-based decision support. He has provided consulting services to numerous entities over the years, including United Way, the Public Health Agency of Canada, the Canadian Air Transport Security Authority, the Royal Canadian Mounted Police, Transport Canada, the Nuclear Waste Management Organization, the Privy Council Office, and Correctional Services Canada.

He has extensive experience in operations research, data science and predictive analytics, stochastic modeling, and simulations – managing and being involved in numerous projects in these subject areas from inception to completion. He also leads various workshops and training courses on data science and statistical analysis through the Data Action Lab.