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Synopsis
This business analytics (BA) text 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.
"synopsis" may belong to another edition of this title.
About the Author
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.
"About this title" may belong to another edition of this title.
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Business Analytics: A Data Driven Decision Making Approach for Business (2)
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Condition: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition.Please note:-May include library or rental stickers, stamps, or markings.-Supplemental materials e.g., CDs, access codes, inserts are not guaranteed.-Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. -Sourced from donation centers; authenticity not verified with publisher. Your satisfaction is our top priority! If you have any questions or concerns about your order, please donât hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading! Seller Inventory # STM.64Q
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Business Analytics, Volume II: A Data Driven Decision Making Approach for Business (Paperback or Softback)
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Business Analytics, Volume II
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Paperback. Condition: New. This business analytics (BA) text 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. Seller Inventory # LU-9781631574795
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