Business Analytics

A thorough and in-depth overview of data analysis with a focus of practical usage using industry-focused examples and accurate use cases.

– Brad D. Messner

The book provides a business-specific, applied introduction to business analytics. It incorporates multiple business disciplines and perspectives so that students can understand ways that algorithms can be applied in business practice. The chapters are organized by application so that students can see multiple implementations of data science concepts.

– Thomas A. Hanson

This is an advanced textbook that provides a practical approach to data analytics, algorithms, and modeling techniques in a business setting.

– Aeron Zentner

One of the greatest strengths of this book is that it focuses on R through a lens of business problems rather than code. The book provides good explanation about the underlying issues, such as loan charge-off, risk analysis, and more.

– Yavuz Keceli

A unique approach to Business Analytics with a focus on different application domains from External Environment Analytics to Supply Chain Analytics.

– Anita Lee-Post

This text would provide for the opportunity to expand the skills of students and offer one a way to broaden the content covered in an advanced undergraduate course or first year graduate course. I think that the coverage of PCA and Text Analysis is particularly good and is becoming more and more mainstream. Thus, these are topics that need to be covered even at the undergraduate level but are difficult to fit into a single course. This text could provide the opportunity deal with that problem.

– Joel Kincaid

Good data analytics text using R that you can customize for program needs based upon discipline focus.

– Kevin S. Walker

This book is well-grounded in practical business decision making and includes straightforward discussion and interpretation of statistical output.

– John L. Sparco

The content of this book is thorough, with each chapter including a case study and R code example.

– Yue Han

Arul Mishra is the Emma Eccles Jones Presidential Chair Professor of Marketing and Adjunct Professor, School of Computing at the University of Utah. Her research, on a broader level, uses machine learning methods to understand customer decisions and guide firm strategies. Specifically, she derives theoretical and practical insights from data using computational algorithms to understand customer engagement in digital markets, customer preference and choice, financial decisions, online advertising, and creativity. Currently her research involves leveraging language and generative models for business applications. She also examines the ethical consequences of using algorithms. Can algorithms exacerbate or reduce the impact of social biases and inequities? How can algorithms help firms make better decisions? Methodologically, she uses Natural Language Processing, generative language models, image processing, and field studies to test social phenomena and theories. Arul’s research has been published in the Journal of Marketing Research, Journal of Consumer Research, Journal of Marketing, Marketing Science, Management Science, Journal of Personality and Social Psychology, Organizational Behavior and Human Decision Processes, Psychological Science, and American Psychologist®. Popular accounts of her work have appeared in Scientific American, Los Angeles Times, The Wall Street Journal, Chicago Tribune, MSN Money, The Financial Express, and Shape. Arul teaches or has taught several courses at the Eccles School of Business including Algorithms for Business Decisions for Master students, Consumer Analytics for undergraduate students, and doctoral courses on research theory and methods.

Himanshu Mishra serves as the David Eccles Professor at the Eccles School of Business and as an Adjunct Professor in the Kahlert School of Computing at the University of Utah. He earned his Ph.D. in marketing from the University of Iowa. Himanshu uses machine learning methods to analyze human decisions in social and marketplace settings. He often collaborates with firms to apply the insights he gathers from research. The findings of his research inform consumer decision-making, AI′s role in fair decisions, risk assessment strategies, and overall human well-being. With over 20 years in academia, Himanshu has taught across undergraduate, graduate, and Ph.D. levels. His recent courses involve using machine learning applications to improve business decisions and the importance of algorithmic fairness. His extensive research contributions can be found in top journals and conferences spanning marketing, business, computer science, and psychology—including the Journal of Marketing Research, IEEE International Conference on Big Data, Psychological Science, and others. Moreover, media outlets like MSNBC, The Wall Street Journal, National Public Radio, and The New York Times have featured his work.