國立高雄大學圖資館 |

Language: English

Back

Business analytics with R and Python

Record Type:	Electronic resources : Monograph/item
Title/Author:	Business analytics with R and Pythonby David L. Olson ... [et al.].
other author:	Olson, David L.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	x, 196 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	BusinessData processing.
Online resource:	https://doi.org/10.1007/978-981-97-4772-6
ISBN:	9789819747726$q(electronic bk.)

Business analytics with R and Python
Business analytics with R and Python[electronic resource] /by David L. Olson ... [et al.]. - Singapore :Springer Nature Singapore :2024. - x, 196 p. :ill. (chiefly col.), digital ;24 cm. - AI for risks,2731-6335. - AI for risks..

Data Mining in Business -- Data Mining Processes -- Data Mining Software -- Association Rules -- Cluster Analysis -- Regression Algorithms in Data Mining -- Classification Tools -- Variable Selection -- Dataset Balancing.

This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.

ISBN: 9789819747726$q(electronic bk.)

Standard No.: 10.1007/978-981-97-4772-6doiSubjects--Topical Terms:

199085
Business
--Data processing.

LC Class. No.: HF5548.2

Dewey Class. No.: 658.05

Business analytics with R and Python
LDR:02309nmm a2200337 a 4500 001 666664
003 DE-He213
005 20240730130240.0
006 m d
007 cr nn 008maaau
008 250107s2024 si s 0 eng d
020 $a 9789819747726$q(electronic bk.)
020 $a 9789819747719$q(paper)
024 7 $a 10.1007/978-981-97-4772-6 $2 doi
035 $a 978-981-97-4772-6
040 $a GP $c GP
041 0 $a eng
050 4 $a HF5548.2
072 7 $a KJQ $2 bicssc
072 7 $a COM039000 $2 bisacsh
072 7 $a KJQ $2 thema
082 0 4 $a 658.05 $2 23
090 $a HF5548.2 $b .B979 2024
245 0 0 $a Business analytics with R and Python $h [electronic resource] / $c by David L. Olson ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a x, 196 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a AI for risks, $x 2731-6335
505 0 $a Data Mining in Business -- Data Mining Processes -- Data Mining Software -- Association Rules -- Cluster Analysis -- Regression Algorithms in Data Mining -- Classification Tools -- Variable Selection -- Dataset Balancing.
520 $a This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.
650 0 $a Business $x Data processing. $3 199085
650 0 $a Data mining. $3 184440
650 0 $a R (Computer program language) $3 210846
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Business Information Systems. $3 274346
700 1 $a Olson, David L. $3 212983
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a AI for risks. $3 931628
856 4 0 $u https://doi.org/10.1007/978-981-97-4772-6
950 $a Computer Science (SpringerNature-11645)