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Handbook of big data analytics
~
Hardle, Wolfgang Karl.
Handbook of big data analytics
Record Type:
Electronic resources : Monograph/item
Title/Author:
Handbook of big data analyticsedited by Wolfgang Karl Hardle, Henry Horng-Shing Lu, Xiaotong Shen.
other author:
Hardle, Wolfgang Karl.
Published:
Cham :Springer International Publishing :2018.
Description:
viii, 538 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Big dataStatistical methods.
Online resource:
http://dx.doi.org/10.1007/978-3-319-18284-1
ISBN:
9783319182841$q(electronic bk.)
Handbook of big data analytics
Handbook of big data analytics
[electronic resource] /edited by Wolfgang Karl Hardle, Henry Horng-Shing Lu, Xiaotong Shen. - Cham :Springer International Publishing :2018. - viii, 538 p. :ill., digital ;24 cm. - Springer handbooks of computational statistics,2197-9790. - Springer handbooks of computational statistics..
Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.
ISBN: 9783319182841$q(electronic bk.)
Standard No.: 10.1007/978-3-319-18284-1doiSubjects--Topical Terms:
716966
Big data
--Statistical methods.
LC Class. No.: QA76.9.B45 / H363 2018
Dewey Class. No.: 005.7
Handbook of big data analytics
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Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.
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Mathematics and Statistics (Springer-11649)
based on 0 review(s)
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EB QA76.9.B45 H236 2018 2018
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http://dx.doi.org/10.1007/978-3-319-18284-1
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