International series in operations research & management science - 9783030433833 - data science and productivity analytics: 290 di charles (13 risultati)
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Lingua: Inglese
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Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Buch. Condizione: 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 scienc…e/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, Naïve 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.
Data Science and Productivity Analytics
Charles, Vincent (Edited by)/ Aparicio, Juan (Edited by)/ Zhu, Joe (Edited by)
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 449 pages. 9.25x6.10x1.20 inches. In Stock.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
- Rilegato
Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Hardcover. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Mai 2020, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: 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, Naïve 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.
Lingua: Inglese
Editore: Springer International Publishing, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
- Rilegato
- Print on Demand
Da: moluna, Greven, Germaniamoluna
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First book to combine DEA and Data ScienceEditors and Contributors at the forefront of field worldwideIllustrates how Data Science techniques can unleash value and drive productivityVincent Charles is an ex…perienced researcher in the fiel.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand.
Altre immaginiLingua: Inglese
Editore: Springer, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
- Rilegato
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Da: preigu, Osnabrück, Germaniapreigu
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Buch. Condizione: Neu. Data Science and Productivity Analytics | Vincent Charles (u. a.) | Buch | x | Englisch | 2020 | Springer | EAN 9783030433833 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Lingua: Inglese
Editore: Springer, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
- Rilegato
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Springer Mai 2020, 2020
Serie: Libro 280 di 323 - International Series in Operations Research & Management Science
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 datä. 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, Naïve 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 452 pp. Englisch.
