Business Analytics, Volume II: A Data Driven Decision Making Approach for Business
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It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together.
The first three chapters provide an introduction to BA, importance of analytics, types of BA-descriptive, predictive, and prescriptive-along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics-machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics.
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Dr. Amar Sahay is a professor of decision sciences engaged in teaching, research, consulting, and training. He holds a BS in production engineering (BIT, India), MS in industrial engineering, and a PhD in mechanical engineering-both from the University of Utah. He has taught and is teaching at several institutions in Utah, including the University of Utah (School of Engineering and Management), SLCC, Westminster College, and others. Amar is a certified Six Sigma Master Black Belt and is also lean manufacturing/lean management certified. He has contributed a number of research papers in national and international journals/proceedings to his credit. Amar has authored around 10 books in the areas of data visualization, business analytics, Six Sigma, statistics and data analysis, modeling, and applied regression. He is also associated with QMS Global LLC, a company engaged in data visualization, analytics, quality, lean six sigma, manufacturing, and systems analysis services. Amar is a senior member of the Industrial & Systems Engineers, the American Society for Quality (ASQ), and Data Science.
