From Data to Decision
A Handbook for the Modern Business Analyst
Published on 19. December 2018
Book
Paperback/Softback
326 pages
978-1-5165-2063-3 (ISBN)
Description
From Data to Decision: A Handbook for the Modern Business Analyst provides readers with a comprehensive guide to understanding the inherent value of business analytics, building critical skill sets to conduct effective analyses, deriving valuable insight from analyses, and guiding management and other personnel toward well-informed, strategic decisions that bolster the health of a company or organization.
The text begins with a chapter that outlines the rise of analytics as a dedicated discipline, its role in business decision-making, and various types of analyses. Additional chapters introduce readers to data strategy, a framework for and process for analytics, and how to apply insights for maximum impact within companies and organizations. Students examine analysis methods including linear regression, logistic regression, decision trees, multi-dimensional scaling, factor analysis, text analytics, time-series analysis, and neural nets. Throughout, readers are challenged to connect the dots between analysis and its effective application within business settings.
A robust guide to modern analysis, From Data to Decision is an ideal textbook for courses in business and analytics, and suitable for both undergraduate and graduate studies.
The text begins with a chapter that outlines the rise of analytics as a dedicated discipline, its role in business decision-making, and various types of analyses. Additional chapters introduce readers to data strategy, a framework for and process for analytics, and how to apply insights for maximum impact within companies and organizations. Students examine analysis methods including linear regression, logistic regression, decision trees, multi-dimensional scaling, factor analysis, text analytics, time-series analysis, and neural nets. Throughout, readers are challenged to connect the dots between analysis and its effective application within business settings.
A robust guide to modern analysis, From Data to Decision is an ideal textbook for courses in business and analytics, and suitable for both undergraduate and graduate studies.
Reviews / Votes
The subtitle of 'From Data to Decision: The Handbook for the Modern Business Analyst' is accurate and says it all. This comprehensive text is a highly valuable, how-to book that covers analytics topics from basic analysis to linear and logistic regression, decision trees, multidimensional scaling, principal component and factor analysis, cluster analysis, times series analysis, a higher-level treatment of advanced neural networks and machine-learning (ML), and finally a how-to chapter on text analysis. [...] I can't recommend this book enough - it balances highly technical, detailed information with practical business explanations and should be used in classrooms, as a reference for centres of excellence and by practitioners and business leaders. This handbook is a must-have." -Excerpted from the book review by Seth Earley, CEO and Founder of Earley Information Science, in the peer-reviewed journal Applied Marketing AnalyticsMore details
Language
English
Place of publication
San Diego
United States
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Weight
633 gr
ISBN-13
978-1-5165-2063-3 (9781516520633)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Marco Vriens is an assistant professor at the University of Wisconsin-La Crosse and the founder of Kwantum, an analytics firm. He earned a masters in psychology from Leiden University and a Ph.D. in business administration from the University of Groningen. He specializes in marketing analytics, brand research, research methodology, and consumer psychology and decision-making. He is the author of The Insights Advantage (2012), and editor of the Handbook of Marketing Research (2006).
Chad Vidden is an associate professor of mathematics and statistics at the University of Wisconsin-La Crosse. He earned his Ph.D. in applied mathematics from Iowa State University. He specializes in machine learning, data science, numerical analysis, and computational mathematics.
Song Chen is an associate professor of mathematics and statistics at the University of Wisconsin-La Crosse. He earned his Ph.D. in applied mathematics from Auburn University. He specializes in data science and scientific computing.
Chad Vidden is an associate professor of mathematics and statistics at the University of Wisconsin-La Crosse. He earned his Ph.D. in applied mathematics from Iowa State University. He specializes in machine learning, data science, numerical analysis, and computational mathematics.
Song Chen is an associate professor of mathematics and statistics at the University of Wisconsin-La Crosse. He earned his Ph.D. in applied mathematics from Auburn University. He specializes in data science and scientific computing.