Data Visualization: Applications in Data Analysis and Quality: Graphical and Quality Tools for Improving Business Process, Product and Service Quality

This second volume of Business Analytics discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes. 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.

Ancillary data sets and more available for downloading on the publisher's website.

Dr. Amar Sahay is a professor engaged in teaching, research, consulting, and training. He has a BS in production engineering from Birla Institute of Technology, India, an MS in industrial engineering and a PhD in mechanical engineering, - both from University of Utah. He has taught/is teaching at several Utah institutions 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 holds expert-level certification in lean manufacturing / and lean management. He has contributed over 30 research papers in national and international conferences. Amar is the author of 11 books and is a senior member of Industrial & Systems Engineers, American Society for Quality, and Data Science Central.