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)
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"Ivezic and colleagues at the University of Washington and the Georgia Institute of Technology have written a comprehensive, accessible, well-thought-out introduction to the new and burgeoning field of astrostatistics. . . . The authors provide another valuable service by discussing how to access data from key astronomical research programs." --Choice

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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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Eljko Ivezic
Editorial: Princeton University Press (2014)
ISBN 10: 0691151687 ISBN 13: 9780691151687
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Descripción Princeton University Press, 2014. HRD. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería WP-9780691151687

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Ivezić, Zeljko; Connolly, Andrew J.; VanderPlas, Jacob T; Gray, Alexander
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Zeljko Ivezic, Andrew J. Connolly, Jacob Vanderplas
Editorial: Princeton University Press, United States (2014)
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Descripción Princeton University Press, United States, 2014. Hardback. Estado de conservación: New. 251 x 180 mm. 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 AAH9780691151687

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Zeljko Ivezic, Andrew J. Connolly, Jacob Vanderplas
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Descripción Princeton University Press, United States, 2014. Hardback. Estado de conservación: New. 251 x 180 mm. 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 AAH9780691151687

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Zeljko Ivezic, Andrew J. Connolly, Jacob T VanderPlas, Alexander Gray
Editorial: Princeton University Press 2014-01-12 (2014)
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Descripción Princeton University Press 2014-01-12, 2014. Estado de conservación: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. Nº de ref. de la librería NU-LBR-01271235

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Zeljko Ivezic
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Descripción Princeton Univers. Press Mrz 2014, 2014. Buch. Estado de conservación: Neu. 261x184x40 mm. Neuware - 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. 540 pp. Englisch. Nº de ref. de la librería 9780691151687

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Zeljko Ivezic, Andrew J. Connolly, Jacob VanderPlas
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Descripción Princeton University Press 2014-02-18, New Jersey, 2014. hardback. Estado de conservación: New. Nº de ref. de la librería 9780691151687

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Descripción Princeton Univers. Press Mrz 2014, 2014. Buch. Estado de conservación: Neu. 261x184x40 mm. Neuware - 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. 540 pp. Englisch. Nº de ref. de la librería 9780691151687

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Zeljko Ivezic
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Descripción Princeton Univers. Press Mrz 2014, 2014. Buch. Estado de conservación: Neu. 261x184x40 mm. Neuware - 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. 540 pp. Englisch. Nº de ref. de la librería 9780691151687

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Zeljko Ivezic
Editorial: Princeton Univers. Press Mrz 2014 (2014)
ISBN 10: 0691151687 ISBN 13: 9780691151687
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Descripción Princeton Univers. Press Mrz 2014, 2014. Buch. Estado de conservación: Neu. 261x184x40 mm. Neuware - 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. 540 pp. Englisch. Nº de ref. de la librería 9780691151687

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