This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book’s affiliated Github.
This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.
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Walter R. Paczkowski earned his Ph.D. in Economics at Texas A&M University and has worked at AT&T's Analytical Support Center, Market Analysis and Forecasting Division, and Business Research Division. He was also a Member of the Technical Staff at AT&T Bell Labs before founding Data Analytics Corp., a statistical consulting and data modeling company, in 2001. Dr. Paczkowski is a part-time lecturer in the Department of Economics and the Department of Statistics at Rutgers University. He published six books in, what he refers to as, his Analytics Series. His latest are Business Analytics: Data Science for Business Problems (Springer, 2021) and Modern Survey Analysis: Using Python for Deeper Insights (Springer, 2022).
This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github.
This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.?
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter not Elektronisches Buch, available at the book's affiliated Github.This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors. 396 pp. Englisch. Nº de ref. del artículo: 9783031318863
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Emphasizes predictive and simulation analytics in complex systemsPresents methods for different system perspectives: operational, tactical, and strategicDemonstrates concepts with programming examples, provided in Jupyter notebooks. Nº de ref. del artículo: 842326679
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter not Elektronisches Buch, available at the book's affiliated Github.This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 396 pp. Englisch. Nº de ref. del artículo: 9783031318863
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