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In Data Science and Data Analysis, as in most technical or quantitative fields of inquiry, there is an important distinction between understanding the theoretical underpinnings of the methods and knowing how and when to best apply them to practical situations.

The successful transition from clean pedagogical toy examples to messy situations can be complicated by a misunderstanding of what a useful and insightful solution looks like in a non-academic context.

In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and conclusions, rather than explain the algorithms or focus on theoretical matters.

Data Science Report Series #3: Data Analysis Case Studies, by Patrick Boily.

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.

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