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Doing Data Science: Straight Talk from the Frontline Paperback – 3 Nov 2013

3.9 out of 5 stars 10 customer reviews

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Frequently bought together

  • Doing Data Science: Straight Talk from the Frontline
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  • Data Science from Scratch: First Principles with Python
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  • Data Science for Business: What you need to know about data mining and data-analytic thinking
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Product details

  • Paperback: 408 pages
  • Publisher: O'Reilly Media; 1 edition (3 Nov. 2013)
  • Language: English
  • ISBN-10: 1449358659
  • ISBN-13: 978-1449358655
  • Product Dimensions: 15.2 x 2 x 22.9 cm
  • Average Customer Review: 3.9 out of 5 stars 10 customer reviews
  • Amazon Bestsellers Rank: 78,167 in Books (See Top 100 in Books)
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Product description

Review

"I enjoyed Rachel and Cathy's book, it's readable, informative, and like no other book I've read on the topic of statistics or data science."
--Andrew Gelman
Professor of statistics and political science, and director of the Applied Statistics Center at Columbia University

"I got a lot out of Doing Data Science, finding the chapter organization on business problem specification, analytics formulation, data access/wrangling, and computer code to be very helpful in understanding DS solutions."
--Steve Miller
Co-founder, OpenBI, LLC, a Chicago-based business intelligence services firm

About the Author

Cathy O’Neil earned a Ph.D. in math from Harvard, was postdoc at the MIT math department, and a professor at Barnard College where she published a number of research papers in arithmetic algebraic geometry. She then chucked it and switched over to the private sector. She worked as a quant for the hedge fund D.E. Shaw in the middle of the credit crisis, and then for RiskMetrics, a risk software company that assesses risk for the holdings of hedge funds and banks. She is currently a data scientist on the New York start-up scene, writes a blog at mathbabe.org, and is involved with Occupy Wall Street.

Rachel Schutt is the Senior Vice President for Data Science at News Corp. She earned a PhD in Statistics from Columbia University, and was a statistician at Google Research for several years. She is an adjunct professor in Columbia’s Department of Statistics and a founding member of the Education Committee for the Institute for Data Sciences and Engineering at Columbia. She holds several pending patents based on her work at Google, where she helped build user-facing products by prototyping algorithms and building models to understand user behavior. She has a master's degree in mathematics from NYU, and a master's degree in Engineering-Economic Systems and Operations Research from Stanford University. Her undergraduate degree is in Honors Mathematics from the University of Michigan.



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