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) 1st Edition

by Željko Ivezić (Author), Andrew J. Connolly (Author), Jacob T. VanderPlas (Author),

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ISBN-13: 978-0691151687

ISBN-10: 0691151687

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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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Editorial Reviews

Review

"Winner of the 2016 IAA Outstanding Publication Award, International Astrostatistics Association"

"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

"A substantial work that can be of value to students and scientists interesting in mining the vast amount of astronomical data collected to date. . . . A well-prepared introduction to this material. . . . If data mining and machine learning fall within your interest area, this text deserves a place on your shelf." ― International Planetarium Society

Review

"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

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

From the Back Cover

"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.

Publisher ‏ : ‎ Princeton University Press; 1st edition (January 12, 2014)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 560 pages
ISBN-10 ‏ : ‎ 0691151687
ISBN-13 ‏ : ‎ 978-0691151687
Item Weight ‏ : ‎ 2.75 pounds
Dimensions ‏ : ‎ 7 x 1.75 x 10 inches

Best Sellers Rank: #1,774,892 in Books (See Top 100 in Books)
- #819 in Astronomy & Astrophysics
- #876 in Artificial Intelligence (Books)
- #1,042 in Data Mining (Books)

Customer Reviews:
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Robert Heaphy

Good book

Reviewed in the United States on September 26, 2020

Verified Purchase

Good general review of important algorithms using Python. Updated edition is available.

Helpful

Carl Rodriguez

An excellent coverage of several methods and techniques used in astrophysics

Reviewed in the United States on March 16, 2015

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An excellent coverage of several methods and techniques used in astrophysics. I wish I'd had this book earlier in my graduate career.

One person found this helpful

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John Smallberries

real-world advice -- you'll find it here

Reviewed in the United States on June 2, 2016

Something that sets this book apart is how different numerical approaches to the same problem are compared. It's often true that you have several methods to choose from, and the best choice depends on the character of your data or expected solution or computational resources. This book does a great job of summarizing tradeoffs in such decisions, and gives insight into making appropriate choices.

3 people found this helpful

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George J. Lees Jr.

Not a Computational Astronomy hands on manual

Reviewed in the United States on January 18, 2015

I am pretty disappointed with this book because i assumed that it was going to walk you through actually doing stats and ML on astronomical data sets. However, all of the codes in the book just show you how to use the astroML functions without using them on actually data sets. Instead the authors just make random variables then throw them into the functions... I would have much more appreciated the steps:
1. Show and explain an astronomical dataset
2. Do stats and ML on those datasets with a full explanation

35 people found this helpful

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Dale Frakes

Which version of Python is this written for?

Reviewed in the United States on March 12, 2014

I can't find an "ask a question" link like other products on Amazon. I read one of the author's blog posts on frequentist vs bayesian statistics and came here that way.

This book looks interesting, but can anyone say what version of Python the code in the book is written for?

3 people found this helpful

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Top reviews from other countries

PeterDz

Recommended.

Reviewed in the United Kingdom on June 28, 2017

Verified Purchase

Highly recommended. A view I know to be shared by a number of colleagues when they started using it in 2015 or thereabouts. The target market is largely postgraduate and professional astrophysicists and cosmologists.

Software is directed towards a Linux/UNIX environment as that is what many Pros use; but it shouldn't be impossible to use under Windows. I simply haven't tried. The book is based around a package created by the authors called AstroML, use of which is straightforward and for which the instructions provided are clear.

There are some gaps and a few errata. The book needs a bit of revision now (see below) but it is an excellent text. It could also benefit from better instructions on how to use - and share - via Jupyter notebooks now that this is largely replacing iPython (the implementation uses AstroML with IPython, so it shouldn't be too difficult). I eventually got it to work in Jupyter notebooks with a bit of tinkering; necessary because the code is Python 2.7-compatible not Python 3.3+. The AstroML code itself needs be updated to Python 3 in order to ensure compatibility with Python going forward. The book also presumes a knowledge of iPython. A bit of attention needs to be given to deprecated features in some of the original code in the book. Lots of links to related websites, databases and additional tools. Plenty of references, too.

In an ideal world a book like this should be the subject of a maintained site from which you could download updates automatically. It isn't.

In general a very good introduction to modern statistical and machine learning techniques for astrophysics and cosmology. What it isn't however is a hands on work through of how to apply the AstroML codes to derive specific results except in a fairly general way, despite having plenty of links to the SDSS database.

While there are areas that need updating in a second edition, none of this should dissuade you from using it if it meets your needs.

sdc

Very usefull

Reviewed in France on February 27, 2016

Verified Purchase

I am having a professional transition from fundamental physics to cosmology. I need to learn other tools, including python language and data analysis tools.
This book , which provides a lot of examples, must also be used with its companion the corresponding web site on astroML.

It is quite easy to install on my computer all the python stuff.
I find very great the encapsulation to retrieve the most recent and very nice astronomic and cosmological data from the big astronomical database.

I think the professional transition is almost done !

Gabriel

Best book in astrostatistics nowadays

Reviewed in the United Kingdom on May 4, 2014

Verified Purchase

This is the best choice for learning modern statistical methods for advance undergraduate and graduate student,s not only for astrophysicists but for any physical science.

3 people found this helpful

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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) 1st Edition

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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) 1st Edition

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