Designing Great Data Products [NOOK Book]

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Overview

In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next step: going from recommendations to products that can produce optimal strategies for meeting concrete business objectives.


We already know how ...

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Designing Great Data Products

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More About This Book

Overview

In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines like Amazon's. Prediction technology can be interesting and mathematically elegant, but we need to take the next step: going from recommendations to products that can produce optimal strategies for meeting concrete business objectives.


We already know how to build these products: they've been in use for the past decade or so, but they're not as common as they should be. This report shows how to take the next step: to go from simple predictions and recommendations to a new generation of data products with the potential to revolutionize entire industries.

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Product Details

  • ISBN-13: 9781449333683
  • Publisher: O'Reilly Media, Incorporated
  • Publication date: 3/23/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Sales rank: 50,617
  • File size: 5 MB

Meet the Author

Jeremy Howard is President and Chief Scientist at Kaggle. Previously, he founded FastMail (sold to Opera Software) and Optimal Decisions sold to ChoicePoint – now called LexisNexis Risk Solutions). Prior to that he worked in management consulting, at McKinsey & Company and A.T. Kearney. Jeremy’s passion is applying algorithms to data. At FastMail he used algorithms to automate nearly every part of the business – as a result the company only needed a total of 3 full time staff, and got over a million signups. Optimal Decisions was a business entirely built to commercialise a new algorithm he designed for the optimal pricing of insurance. Jeremy competes regularly in data mining competitions, which he uses to test himself and stay on the leading edge of machine learning and predictive modelling technology. He is currently ranked #1 on Kaggle’s overall competitor rankings, out of over 16,000 data scientists.

Margit Pavlath Zwemer is a Data Scientist and Community Manager at Kaggle and an organizer for Data Science Global. She is a recovering high-frequency volatility trader, formerly based in Hong Kong, a graduate of the Berkeley Master of Financial Engineering program (MFE) , and did her undergrad in Mathematics at Stanford. Her love of algorithms dates back to a rainy afternoon in the '90s when she discovered Conway's Game of Life.

Mike Loukides is an editor for O'Reilly & Associates. He is the author of System Performance Tuning and UNIX for FORTRAN Programmers. Mike's interests are system administration, networking, programming languages, and computer architecture. His academic background includes degrees in electrical engineering (B.S.) and English literature (Ph.D.).

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  • Anonymous

    Posted June 23, 2012

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    Posted April 23, 2012

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