andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1014 knowledge-graph by maker-knowledge-mining
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Introduction: Cathy O’Neil organized this visualization project with NYPD stop-and-frisk data. It’s part of the Data Without Borders project. Unfortunately, because of legal restrictions I couldn’t send them the data Jeff, Alex, and I used in our project several years ago.
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2 Unfortunately, because of legal restrictions I couldn’t send them the data Jeff, Alex, and I used in our project several years ago. [sent-3, score-1.404]
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same-blog 1 1.0 1014 andrew gelman stats-2011-11-16-Visualizations of NYPD stop-and-frisk data
Introduction: Cathy O’Neil organized this visualization project with NYPD stop-and-frisk data. It’s part of the Data Without Borders project. Unfortunately, because of legal restrictions I couldn’t send them the data Jeff, Alex, and I used in our project several years ago.
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Introduction: Rachel Schutt and Cathy O’Neil just came out with a wonderfully readable book on doing data science, based on a course Rachel taught last year at Columbia. Rachel is a former Ph.D. student of mine and so I’m inclined to have a positive view of her work; on the other hand, I did actually look at the book and I did find it readable! What do I claim is the least important part of data science? Here’s what Schutt and O’Neil say regarding the title: “Data science is not just a rebranding of statistics or machine learning but rather a field unto itself.” I agree. There’s so much that goes on with data that is about computing, not statistics. I do think it would be fair to consider statistics (which includes sampling, experimental design, and data collection as well as data analysis (which itself includes model building, visualization, and model checking as well as inference)) as a subset of data science. The question then arises: why do descriptions of data science focus so
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Introduction: Stephanie Evergreen writes: Media, web design, and marketing have all created an environment where stakeholders – clients, program participants, funders – all expect high quality graphics and reporting that effectively conveys the valuable insights from evaluation work. Some in statistics and mathematics have used data visualization strategies to support more useful reporting of complex ideas. Global growing interest in improving communications has begun to take root in the evaluation field as well. But as anyone who has sat through a day’s worth of a conference or had to endure a dissertation-worthy evaluation report knows, evaluators still have a long way to go. To support the development of researchers and evaluators, some members of the American Evaluation Association are proposing a new TIG (Topical Interest Group) on Data Visualization and Reporting. If you are a member of AEA (or want to be) and you are interested in joining this TIG, contact Stephanie Evergreen.
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Introduction: Cathy O’Neil organized this visualization project with NYPD stop-and-frisk data. It’s part of the Data Without Borders project. Unfortunately, because of legal restrictions I couldn’t send them the data Jeff, Alex, and I used in our project several years ago.
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