Applying predictive analytics: finding value in data

By:

McCarthy, Richard V

Contributor(s):

Material type: Text

TextPublication details: Cham Springer 2022Edition: 2ndDescription: xv, 274 pISBN:

9783030830724

Subject(s):

DDC classification:

658.83 MCC

Summary: The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project. (https://link.springer.com/book/10.1007/978-3-030-83070-0)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	658.83 MCC (Browse shelf(Opens below))	1	Available		009030

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Previous	658.816 VOH Principles of pricing: an analytical approach	658.81605 RAO Advanced introduction to pricing strategy and analytics	658.827 CAR Luxury brand and art collaborations: postmodern consumer culture	658.83 MCC Applying predictive analytics: finding value in data	658.8342 CAR Marketing analytics:	658.87 GUS Retailing logistics and fresh food packaging: managing change in the supply chain	658.872 ISE Social media for small business: marketing strategies for business owners	Next

Table of contents:
Front Matter
Pages i-xv
Download chapter PDF
Introduction to Predictive Analytics
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 1-26
Know Your Data: Data Preparation
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 27-54
What Do Descriptive Statistics Tell Us
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 55-85
Predictive Models Using Regression
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 87-121
The Second of the Big 3: Decision Trees
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 123-144
The Third of the Big 3: Neural Networks
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 145-173
Model Comparisons and Scoring
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 175-198
Finding Associations in Data Through Cluster Analysis
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 199-232
Text Analytics: Using Qualitative Data to Support Quantitative Results
Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
Pages 233-254

[https://link.springer.com/book/10.1007/978-3-030-83070-0]

The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.

(https://link.springer.com/book/10.1007/978-3-030-83070-0)

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