andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1837 knowledge-graph by maker-knowledge-mining
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Introduction: Rachel Schutt writes: The hype surrounding Big Data and Data Science is at a fever pitch with promises to solve the world’s business and social problems, large and small. How accurate or misleading is this message? How is it helping or damaging people, and which people? What opportunities exist for data nerds and entrepreneurs that examine the larger issues with a skeptical view? This Meetup focuses on mathematical, ethical, and business aspects of data from a skeptical perspective. Guest speakers will discuss the misuse of and best practices with data, common mistakes people make with data and ways to avoid them, how to deal with intentional gaming and politics surrounding mathematical modeling, and taking into account the feedback loops and wider consequences of modeling. We will take deep dives into models in the fields of Data Science, statistics, financial engineering, and economics. This is an independent forum and open to anyone sharing an interest in the larger use of
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2 How is it helping or damaging people, and which people? [sent-3, score-0.268]
3 What opportunities exist for data nerds and entrepreneurs that examine the larger issues with a skeptical view? [sent-4, score-1.175]
4 This Meetup focuses on mathematical, ethical, and business aspects of data from a skeptical perspective. [sent-5, score-0.811]
5 Guest speakers will discuss the misuse of and best practices with data, common mistakes people make with data and ways to avoid them, how to deal with intentional gaming and politics surrounding mathematical modeling, and taking into account the feedback loops and wider consequences of modeling. [sent-6, score-2.143]
6 We will take deep dives into models in the fields of Data Science, statistics, financial engineering, and economics. [sent-7, score-0.273]
7 This is an independent forum and open to anyone sharing an interest in the larger use of data. [sent-8, score-0.448]
8 Technical aspects will be discussed, but attendees do not need to have a technical background. [sent-9, score-0.499]
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Introduction: Rachel Schutt writes: The hype surrounding Big Data and Data Science is at a fever pitch with promises to solve the world’s business and social problems, large and small. How accurate or misleading is this message? How is it helping or damaging people, and which people? What opportunities exist for data nerds and entrepreneurs that examine the larger issues with a skeptical view? This Meetup focuses on mathematical, ethical, and business aspects of data from a skeptical perspective. Guest speakers will discuss the misuse of and best practices with data, common mistakes people make with data and ways to avoid them, how to deal with intentional gaming and politics surrounding mathematical modeling, and taking into account the feedback loops and wider consequences of modeling. We will take deep dives into models in the fields of Data Science, statistics, financial engineering, and economics. This is an independent forum and open to anyone sharing an interest in the larger use of
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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: After reading Rachel and Cathy’s book , I wrote that “Statistics is the least important part of data science . . . I think it would be fair to consider statistics as a subset of data science. . . . it’s not the most important part of data science, or even close.” But then I received “Data Science for Business,” by Foster Provost and Tom Fawcett, in the mail. I might not have opened the book at all (as I’m hardly in the target audience) but for seeing a blurb by Chris Volinsky, a statistician whom I respect a lot. So I flipped through the book and it indeed looked pretty good. It moves slowly but that’s appropriate for an intro book. But what surprised me, given the book’s title and our recent discussion on the nature of data science, was that the book was 100% statistics! It had some math (for example, definitions of various distance measures), some simple algebra, some conceptual graphs such as ROC curve, some tables and graphs of low-dimensional data summaries—but almost
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