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Principles of data science

Arabnia, Hamid R.

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Principles of data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Principles of data science/ edited by Hamid R. Arabnia ... [et al.].
other author:	Arabnia, Hamid R.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xiv, 278 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Data Acquisition, Extraction, and Cleaning -- Data Summarization and Modeling -- Data Analysis and Communication Techniques -- Data Science Tools -- Deep Learning in Data Science -- Data Science Applications -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-3-030-43981-1
ISBN:	9783030439811

Principles of data science
Principles of data science[electronic resource] /edited by Hamid R. Arabnia ... [et al.]. - Cham :Springer International Publishing :2020. - xiv, 278 p. :ill., digital ;24 cm. - Transactions on computational science and computational intelligence,2569-7072. - Transactions on computational science and computational intelligence..

Introduction -- Data Acquisition, Extraction, and Cleaning -- Data Summarization and Modeling -- Data Analysis and Communication Techniques -- Data Science Tools -- Deep Learning in Data Science -- Data Science Applications -- Conclusion.

This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists' preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice.

ISBN: 9783030439811

Standard No.: 10.1007/978-3-030-43981-1doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45 / P756 2020

Dewey Class. No.: 005.7

Principles of data science
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020 $a 9783030439811 $q (electronic bk.)
020 $a 9783030439804 $q (paper)
024 7 $a 10.1007/978-3-030-43981-1 $2 doi
035 $a 978-3-030-43981-1
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245 0 0 $a Principles of data science $h [electronic resource] / $c edited by Hamid R. Arabnia ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiv, 278 p. : $b ill., digital ; $c 24 cm.
490 1 $a Transactions on computational science and computational intelligence, $x 2569-7072
505 0 $a Introduction -- Data Acquisition, Extraction, and Cleaning -- Data Summarization and Modeling -- Data Analysis and Communication Techniques -- Data Science Tools -- Deep Learning in Data Science -- Data Science Applications -- Conclusion.
520 $a This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists' preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice.
650 0 $a Big data. $3 2045508
650 0 $a Data mining. $3 562972
650 0 $a Statistics $x Data processing. $3 535534
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Big Data/Analytics. $3 2186785
700 1 $a Arabnia, Hamid R. $3 1243983
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Transactions on computational science and computational intelligence. $3 3227107
856 4 0 $u https://doi.org/10.1007/978-3-030-43981-1
950 $a Engineering (SpringerNature-11647)