Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Željko Ivezic, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray

Book Details:

ISBN: 0691151687
EAN: 9780691151687
ASIN: 0691151687
Publisher: Princeton college Press
Publication Date: 2014-01-12
Number of Pages: 560
Website: Amazon, LibraryThing, Google Books, Goodreads

Synopsis from Amazon:

As telescopes, detectors, and desktops develop ever extra strong, the amount of knowledge on the disposal of astronomers and astrophysicists will input the petabyte area, offering exact measurements for billions of celestial items. This e-book presents a finished and available advent to the state of the art statistical tools had to successfully study advanced information units from astronomical surveys reminiscent of the Panoramic Survey Telescope and swift reaction approach, the darkish power Survey, and the impending huge Synoptic Survey Telescope. It serves as a pragmatic guide for graduate scholars and complex undergraduates in physics and astronomy, and as an vital reference for researchers.

Statistics, information Mining, and computing device studying in Astronomy provides a wealth of useful research difficulties, evaluates strategies for fixing them, and explains the best way to use numerous ways for various kinds and sizes of knowledge units. For all purposes defined within the e-book, Python code and instance info units are supplied. The helping information units were conscientiously chosen from modern astronomical surveys (for instance, the Sloan electronic Sky Survey) and are effortless to obtain and use. The accompanying Python code is publicly on hand, good documented, and follows uniform coding criteria. jointly, the information units and code permit readers to breed all of the figures and examples, review the equipment, and adapt them to their very own fields of interest.

  • Describes the main valuable statistical and data-mining tools for extracting wisdom from large and complicated astronomical facts sets
  • Features real-world facts units from modern astronomical surveys
  • Uses a freely to be had Python codebase throughout
  • Ideal for college kids and dealing astronomers

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