Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy)

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9780691151687: Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy)

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers.



Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest.



  • Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets

  • Features real-world data sets from contemporary astronomical surveys

  • Uses a freely available Python codebase throughout

  • Ideal for students and working astronomers

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From the Inside Flap:


"This comprehensive book is surely going to be regarded as one of the foremost texts in the new discipline of astrostatistics."--Joseph M. Hilbe, president of the International Astrostatistics Association


"In the era of data-driven science, many students and researchers have faced a barrier to entry. Until now, they have lacked an effective tutorial introduction to the array of tools and code for data mining and statistical analysis. The comprehensive overview of techniques provided in this book, accompanied by a Python toolbox, free readers to explore and analyze the data rather than reinvent the wheel."--Tony Tyson, University of California, Davis


"The authors are leading experts in the field who have utilized the techniques described here in their own very successful research. Statistics, Data Mining, and Machine Learning in Astronomy is a book that will become a key resource for the astronomy community."--Robert J. Hanisch, Space Telescope Science Institute


About the Author:

Željko Ivezić is professor of astronomy at the University of Washington. Andrew J. Connolly is professor of astronomy at the University of Washington. Jacob T. VanderPlas is an NSF postdoctoral research fellow in astronomy and computer science at the University of Washington. Alexander Gray is professor of computer science at Georgia Institute of Technology.

"Sobre este título" puede pertenecer a otra edición de este libro.

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Ivezic, Zeljko, Connolly, Andrew J., VanderPlas, Jacob T, Gray, Alexander
Editorial: Princeton University Press (2014)
ISBN 10: 0691151687 ISBN 13: 9780691151687
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Descripción Princeton University Press, 2014. Estado de conservación: New. Provides an introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. Series: Princeton Series in Modern Observational Astronomy. Num Pages: 552 pages, 12 color illus. 2 halftones. 173 line illus. BIC Classification: PBT; PGG; UNF; UYQM. Category: (P) Professional & Vocational. Dimension: 184 x 256 x 39. Weight in Grams: 1348. . 2014. Hardcover. . . . . . Nº de ref. de la librería V9780691151687

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Descripción Princeton University Press. Estado de conservación: New. Provides an introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. Series: Princeton Series in Modern Observational Astronomy. Num Pages: 552 pages, 12 color illus. 2 halftones. 173 line illus. BIC Classification: PBT; PGG; UNF; UYQM. Category: (P) Professional & Vocational. Dimension: 184 x 256 x 39. Weight in Grams: 1348. . 2014. Hardcover. . . . . Books ship from the US and Ireland. Nº de ref. de la librería V9780691151687

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Descripción Princeton University Press, United States, 2014. Hardback. Estado de conservación: New. Language: English . Brand New Book. As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. * Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets * Features real-world data sets from contemporary astronomical surveys * Uses a freely available Python codebase throughout * Ideal for students and working astronomers. Nº de ref. de la librería AAU9780691151687

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Descripción Princeton University Press, United States, 2014. Hardback. Estado de conservación: New. Language: English . Brand New Book. As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers. Nº de ref. de la librería AAU9780691151687

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?eljko Ivezi?; Andrew J. Connolly; Jacob T VanderPlas; Alexander Gray
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Descripción Princeton University Press, 2014. Hardcover. Estado de conservación: New. book. Nº de ref. de la librería 0691151687

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Eljko Ivezic, Andrew Connolly, Jacob Vanderplas, Alexander Gray, Andrew J. Connolly, Jacob T Vanderplas, Alexander Gray
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Descripción Princeton University Press 2014-02-18, Princeton, 2014. hardback. Estado de conservación: New. Nº de ref. de la librería 9780691151687

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?eljko Ivezi?; Andrew J. Connolly; Jacob T VanderPlas; Alexander Gray
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