Business Analytics by Arul Mishra

£103.00
Out of stock
Product Ref. (e.g. ISBN, SKU)
9781071815236
Published:
11 Sep 2024
Format:
Paperback
Number of pages:
344 pages
Dimensions:
231x187mm
Weight:
0.570kgs
Author:
Business Analytics: Solving Business Problems with R offers a practical, hands-on introduction to analytical methods, including machine learning in real-world business scenarios. Connecting business decisions and analytical methods across multiple fields, this book guides readers through a wide range of business problems and their fitting analytical solutions, offering examples and implementation using R.
Businesses typically encounter problems first and then seek out analytical methods to help in decision making. Business Analytics: Solving Business Problems with R by Arul Mishra and Himanshu Mishra offers practical, data-driven solutions for today′s dynamic business environment. This text helps students see the real-world potential of analytical methods to help meet their business challenges by demonstrating the application of crucial methods. These methods are cutting edge, including neural nets, natural language processing, and boosted decision trees. Applications throughout the book, including pricing models, social sentiment analysis, and branding show students how to use these analytical methods in real business settings, including Frito-Lay, Netflix, and Zappos. Step-by-step R code with commentary gives readers the tools to adapt each method to their business settings. The book offers comprehensive coverage across diverse business domains, including finance, marketing, human resources, operations, and accounting. Finally, an entire chapter explores equity and fairness in analytical methods, as well as the techniques that can be used to mitigate biases and enhance equity in the results.

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About the Author: 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. 

More Information
Number of pages 344

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