國立虎尾科技大學 |

Principles of Data Science

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Principles of Data Science/ edited by Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau.
其他作者:	Brüssau, Kai.
面頁冊數:	XIV, 276 p. 102 illus., 55 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Big Data/Analytics. -
電子資源:	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, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau. - 1st ed. 2020. - XIV, 276 p. 102 illus., 55 illus. in color.online resource. - 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:

1106909
Big Data/Analytics.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Principles of Data Science
LDR:02772nam a22004095i 4500 001 1019309
003 DE-He213
005 20201009135539.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030439811 $9 978-3-030-43981-1
024 7 $a 10.1007/978-3-030-43981-1 $2 doi
035 $a 978-3-030-43981-1
050 4 $a TK1-9971
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
245 1 0 $a Principles of Data Science $h [electronic resource] / $c edited by Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XIV, 276 p. 102 illus., 55 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 2 4 $a Big Data/Analytics. $3 1106909
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Information Storage and Retrieval. $3 593926
650 2 4 $a Computational Intelligence. $3 768837
650 1 4 $a Communications Engineering, Networks. $3 669809
650 0 $a Big data. $3 981821
650 0 $a Pattern recognition. $3 1253525
650 0 $a Information storage and retrieval. $3 1069252
650 0 $a Computational intelligence. $3 568984
650 0 $a Electrical engineering. $3 596380
700 1 $a Brüssau, Kai. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1314550
700 1 $a Heilig, Leonard. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1314549
700 1 $a Soviany, Cristina. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1314548
700 1 $a Stahlbock, Robert. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1262029
700 1 $a Daimi, Kevin. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1290488
700 1 $a Arabnia, Hamid R. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 785011
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030439804
776 0 8 $i Printed edition: $z 9783030439828
776 0 8 $i Printed edition: $z 9783030439835
830 0 $a Transactions on Computational Science and Computational Intelligence, $x 2569-7072 $3 1310143
856 4 0 $u https://doi.org/10.1007/978-3-030-43981-1
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)