Data Science Productivity Analytics (26 results)
Language: English
Published by Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Published by Springer, 2020
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Published by Springer, 2021
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Language: English
Published by Springer, 2020
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Published by Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Published by Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Softcover
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Taschenbuch. Condition: Neu. Data Science and Productivity Analytics | Vincent Charles (u. a.) | Taschenbuch | x | Englisch | 2021 | Springer | EAN 9783030433864 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer International Publishing, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Hardcover
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Buch. Condition: 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, 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.
Language: English
Published by Springer International Publishing, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: 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, 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 (Editor)/ Aparicio, Juan (Editor)/ Zhu, Joe (Editor)
Language: English
Published by Springer Nature, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Softcover
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Paperback. Condition: Brand New. 452 pages. 9.25x6.10x1.07 inches. In Stock.
Data Science and Productivity Analytics
Charles, Vincent (Edited by)/ Aparicio, Juan (Edited by)/ Zhu, Joe (Edited by)
Language: English
Published by Springer, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Hardcover
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Language: English
Published by Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Language: English
Published by Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Language: English
Published by Springer, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Hardcover
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Language: English
Published by Springer, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Language: English
Published by Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Softcover
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Language: English
Published by Springer International Publishing Mai 2020, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Buch. Condition: 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' a…nd '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.
Language: English
Published by Springer International Publishing Mai 2021, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: 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 anal…ysis' 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.
Language: English
Published by Springer International Publishing, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Gebunden. Condition: 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 exp…erienced researcher in the fiel.
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Kartoniert / Broschiert. Condition: 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 prod…uctivity analysis/data envelopment analysis and data scienc.
Language: English
Published by Springer, 2021
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Language: English
Published by Springer, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
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Published by Springer, 2021
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Published by Springer, Springer Mai 2021, 2021
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Taschenbuch. Condition: 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.
Language: English
Published by Springer, Springer Mai 2020, 2020
Series: International Series in Operations Research & Management Science, Book 280 of 323. Book 280 of 323 - International Series in Operations Research & Management Science
- Hardcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Buch. Condition: 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.
