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Statistics, Data Mining, and Machine Learning in AstronomyA Practical Python Guide for the Analysis of Survey Data$
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Željko Ivezic, Andrew J. Connolly, Jacob T VanderPlas, and Alexander Gray

Print publication date: 2014

Print ISBN-13: 9780691151687

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691151687.001.0001

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About the Book and Supporting Material

About the Book and Supporting Material

Chapter:
(p.3) 1 About the Book and Supporting Material
Source:
Statistics, Data Mining, and Machine Learning in Astronomy
Author(s):

Željko Ivezi

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Željko Ivezi

Andrew J. Connolly

Jacob T. VanderPlas

Alexander Gray

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691151687.003.0001

This chapter begins by discussing the meaning of data mining, machine learning, and knowledge discovery. Data mining, machine learning, and knowledge discovery refer to research areas which can all be thought of as outgrowths of multivariate statistics. Their common themes are analysis and interpretation of data, often involving large quantities of data, and even more often resorting to numerical methods. The chapter then presents an incomplete survey of the relevant literature following by an introduction to the Python programming language and the Git code management tool. Next, it describes the surveys and data sets used in examples, plotting and visualizing the data in this book, and how to efficiently use this book.

Keywords:   Python programming language, Git code management, data mining, machine learning, knowledge discovery

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