From Data to Decision

A Handbook for the Modern Business Analyst (First Edition)
Marco Vriens, Song Chen, and Chad Vidden
Paperback, 326 pages
ISBN: 978-1-5165-2063-3 ©2019
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From Data to Decision

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Summary
    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 Second Edition will be published Fall 2020. ISBN: 978-1-5165-9811-3

    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.