國立高雄大學圖資館 |

Language: English

Back

Foundations of data science for engineering problem solving

Record Type:	Electronic resources : Monograph/item
Title/Author:	Foundations of data science for engineering problem solvingby Parikshit Narendra Mahalle ... [et al.].
other author:	Mahalle, Parikshit Narendra.
Published:	Singapore :Springer Singapore :2022.
Description:	xiv, 117 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	EngineeringData processing.
Online resource:	https://doi.org/10.1007/978-981-16-5160-1
ISBN:	9789811651601$q(electronic bk.)

Foundations of data science for engineering problem solving
Foundations of data science for engineering problem solving[electronic resource] /by Parikshit Narendra Mahalle ... [et al.]. - Singapore :Springer Singapore :2022. - xiv, 117 p. :ill., digital ;24 cm. - Studies in big data,v. 942197-6511 ;. - Studies in big data ;v.1..

Introduction to Data Science -- Data Collection and Preparation -- Data Analysis and Machine learning Algorithms -- Data Visualization Tools and Data Modelling -- Data Science in Information, Communication and Technology -- Data Science in Civil & Mechanical Engineering -- Data Science in Clinical Decision System -- Conclusions.

This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.

ISBN: 9789811651601$q(electronic bk.)

Standard No.: 10.1007/978-981-16-5160-1doiSubjects--Topical Terms:

180008
Engineering
--Data processing.

LC Class. No.: TA345

Dewey Class. No.: 620.0015

Foundations of data science for engineering problem solving
LDR:02719nmm a2200337 a 4500 001 618125
003 DE-He213
005 20210821185037.0
006 m d
007 cr nn 008maaau
008 221025s2022 si s 0 eng d
020 $a 9789811651601$q(electronic bk.)
020 $a 9789811651595$q(paper)
024 7 $a 10.1007/978-981-16-5160-1 $2 doi
035 $a 978-981-16-5160-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TA345
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 620.0015 $2 23
090 $a TA345 $b .F771 2022
245 0 0 $a Foundations of data science for engineering problem solving $h [electronic resource] / $c by Parikshit Narendra Mahalle ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xiv, 117 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6511 ; $v v. 94
505 0 $a Introduction to Data Science -- Data Collection and Preparation -- Data Analysis and Machine learning Algorithms -- Data Visualization Tools and Data Modelling -- Data Science in Information, Communication and Technology -- Data Science in Civil & Mechanical Engineering -- Data Science in Clinical Decision System -- Conclusions.
520 $a This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
650 0 $a Engineering $x Data processing. $3 180008
650 0 $a Big data. $3 609582
650 0 $a Information visualization. $3 248886
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Computer Science, general. $3 274540
650 2 4 $a Data Engineering. $3 839346
650 2 4 $a Optimization. $3 274084
700 1 $a Mahalle, Parikshit Narendra. $3 917737
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Studies in big data ; $v v.1. $3 675357
856 4 0 $u https://doi.org/10.1007/978-981-16-5160-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)