HOME > Detail View

Detail View

Business analytics using R - A practical approach [electronic resource]

Business analytics using R - A practical approach [electronic resource]

Material type
E-Book(소장)
Personal Author
Hodeghatta, Umesh R. Nayak, Umesh.
Title Statement
Business analytics using R - A practical approach [electronic resource] / Umesh R. Hodeghatta, Umesh Nayak.
Publication, Distribution, etc
Cham :   Springer,   c2017.  
Berkeley, CA :   Apress,   c2017.  
Physical Medium
1 online resource (xvii, 280 p.) : ill.
ISBN
9781484225134 9781484225141 (e-book)
요약
Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. You will: Write R programs to handle data Build analytical models and draw useful inferences from them Discover the basic concepts of data mining and machine learning Carry out predictive modeling Define a business issue as an analytical problem.
General Note
Title from e-Book title page.  
Bibliography, Etc. Note
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Business --Data processing. Management information systems. R (Computer program language).
Short cut
URL
000 00000cam u2200205 a 4500
001 000046011840
005 20200122114022
006 m d
007 cr
008 200107s2017 caua ob 001 0 eng d
020 ▼a 9781484225134
020 ▼a 9781484225141 (e-book)
040 ▼a 211009 ▼c 211009 ▼d 211009
050 0 0 ▼a HF5548.2
082 0 0 ▼a 658.4/0380285513 ▼2 23
084 ▼a 658.40380285 ▼2 DDCK
090 ▼a 658.40380285
100 1 ▼a Hodeghatta, Umesh R.
245 1 0 ▼a Business analytics using R - A practical approach ▼h [electronic resource] / ▼c Umesh R. Hodeghatta, Umesh Nayak.
260 ▼a Cham : ▼b Springer, ▼c c2017.
260 ▼a Berkeley, CA : ▼b Apress, ▼c c2017.
300 ▼a 1 online resource (xvii, 280 p.) : ▼b ill.
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
520 ▼a Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. You will: Write R programs to handle data Build analytical models and draw useful inferences from them Discover the basic concepts of data mining and machine learning Carry out predictive modeling Define a business issue as an analytical problem.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Business ▼x Data processing.
650 0 ▼a Management information systems.
650 0 ▼a R (Computer program language).
700 1 ▼a Nayak, Umesh.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-1-4842-2514-1
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 658.40380285 Accession No. E14018781 Availability Loan can not(reference room) Due Date Make a Reservation Service M

New Arrivals Books in Related Fields

Robbins, Stephen P. (2021)