Additional information
by Amar Sahay (Author)
Business Analytics: A Data-Driven Decision Making Approach for Business-Part I, /i> provides an overview of business analytics (BA), business intelligence (BI), and the role and importance of these in the modern business decision-making.
The book discusses all these areas along with three main analytics categories: (1) descriptive, (2) predictive, and (3) prescriptive analytics with their tools and applications in business. This volume focuses on descriptive analytics that involves the use of descriptive and visual or graphical methods, numerical methods, as well as data analysis tools, big data applications, and the use of data dashboards to understand business performance.
The highlights of this volume are: Business analytics at a glance; Business intelligence (BI), data analytics; Data, data types, descriptive analytics; Data visualization tools; Data visualization with big data; Descriptive analytics-numerical methods; Case analysis with computer applications.
Author Biography
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.
