Softcover. Condici�n: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany. N� de ref. del art�culo: CEA-2410C-FROSCH-03-1000

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Data Science and Productivity Analytics (International Series in Operations Research & Management Science)

Publicado por Springer, 2021

ISBN 10: 3030433862 ISBN 13: 9783030433864

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Data Science and Productivity Analytics (International Series in Operations Research & Management Science)

Publicado por Springer, 2021

ISBN 10: 3030433862 ISBN 13: 9783030433864

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Data Science and Productivity Analytics

Vincent Charles

Publicado por Springer International Publishing Mai 2021, 2021

ISBN 10: 3030433862 ISBN 13: 9783030433864

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Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of 'productivity analysis/data envelopment analysis' and 'data science/big data'. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.Examples of data science techniques include linear and logistic regressions, decision trees, Na�ve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubtthat nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis. 452 pp. Englisch. N� de ref. del art�culo: 9783030433864

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Data Science and Productivity Analytics

Publicado por Springer, Berlin|Springer International Publishing|Springer, 2021

ISBN 10: 3030433862 ISBN 13: 9783030433864

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Kartoniert / Broschiert. Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of productivity analysis/data envelopment analysis and data scienc. N� de ref. del art�culo: 467591177

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Data Science and Productivity Analytics

Vincent Charles

Publicado por Springer International Publishing, 2021

ISBN 10: 3030433862 ISBN 13: 9783030433864

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Taschenbuch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of 'productivity analysis/data envelopment analysis' and 'data science/big data'. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.Examples of data science techniques include linear and logistic regressions, decision trees, Na�ve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubtthat nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis. N� de ref. del art�culo: 9783030433864

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