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Can I read Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy (1)) on EtoBox?
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy (1)) by Zeljko Ivezic, Andrew J. Connolly, Jacob T VanderPlas, Alexander Gray is a nonfiction available to read on EtoBox.
What is Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy (1)) about?
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 p
Who reads Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy (1))?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Zeljko Ivezic, Andrew J. Connolly, Jacob T VanderPlas, Alexander Gray
- Publisher
- Princeton University Press
- Published
- 2014
- Language
- EN
- ISBN
- 9781400848911
- Category
- nonfiction
- Subjects
- Science, Mathematics, Astronomy
Other editions & translations
- Ivezic, Ž ; Statistics, Data Mining, and Machine Learning in (Princeton Series in Modern Observational Astronomy) (2019)
- Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Moder (2020)
- A Practical Python Guide for the Analysis of Survey Data, Updated Edition (With Bookmarks) (2020)
- Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Moder (2019)
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