Artículos relacionados a Data Science and Productivity Analytics: 290 (International...
Data Science and Productivity Analytics: 290 (International Series in Operations Research & Management Science) - Tapa dura
Sinopsis
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 doubt that 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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Vincent Charles is an experienced researcher in the field of Artificial Intelligence and Management Science, currently with the School of Management, University of Bradford. He has more than two decades of teaching, research, and consultancy experience, having been a full professor and director of research for more than a decade. He has published over 130 research outputs. He is a recipient of many international academic honours and awards.
Juan Aparicio is an Associate Professor at the Department of Statistics, Mathematics an Information Technology of the University Miguel Hernandez, Elche (Alicante), Spain. He is the director of the Center of Operations Research and is also Co-Chair (with Knox Lovell) of the Santander Chair on Efficiency and Productivity. He has published over 100 research contributions, mainly on Data Envelopment Analysis, Efficiency and Productivity Analysis.
Joe Zhu is Professor of Operations Analytics in the Foisie Business School, Worcester Polytechnic Institute. He is an internationally recognized expert in methods of performance evaluation and benchmarking using Data Envelopment Analysis (DEA), and his research interests are in the areas of operations and business analytics, productivity modeling, and performance evaluation and benchmarking. He has published and co-edited several books focusing on performance evaluation and benchmarking using DEA and developed the DEA Frontier software.
De la contraportada
<p>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.</p><p>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 doubt that 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.</p><p>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.</p><div><p></p></div>
"Sobre este título" puede pertenecer a otra edición de este libro.
Comprar nuevo
Ver este artículo
EUR 148,31
EUR 7,66 gastos de envío en Estados Unidos de America
Destinos, gastos y plazos de envíoOtras ediciones populares con el mismo título
Resultados de la búsqueda para Data Science and Productivity Analytics: 290 (International...
Imagen de archivo
Data Science and Productivity Analytics (International Series in Operations Research & Management Science, 290)
Librería: Best Price, Torrance, CA, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. SUPER FAST SHIPPING. Nº de ref. del artículo: 9783030433833
Cantidad disponible: 2 disponibles
Imagen de archivo
Data Science and Productivity Analytics (International Series in Operations Research & Management Science, 290)
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020017608
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Data Science and Productivity Analytics (International Series in Operations Research & Management Science, 290)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9783030433833_new
Gastos de envío:
EUR 13,85
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Data Science and Productivity Analytics
Publicado por
Springer International Publishing Mai 2020, 2020
ISBN 10: 3030433838
ISBN 13: 9783030433833
Nuevo
Tapa dura
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783030433833
Gastos de envío:
EUR 23,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 2 disponibles
Imagen del vendedor
Data Science and Productivity Analytics
Publicado por
Springer International Publishing, 2020
ISBN 10: 3030433838
ISBN 13: 9783030433833
Nuevo
Tapa dura
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Gebunden. Condición: 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 experienced researcher in the fiel. Nº de ref. del artículo: 448682072
Gastos de envío:
EUR 48,99
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Data Science and Productivity Analytics (International Series in Operations Research & Management Science)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Nº de ref. del artículo: 26376515684
Cantidad disponible: 4 disponibles
Imagen de archivo
Data Science and Productivity Analytics (International Series in Operations Research & Management Science)
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Print on Demand. Nº de ref. del artículo: 369529787
Gastos de envío:
EUR 7,51
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 4 disponibles
Imagen de archivo
Data Science and Productivity Analytics (International Series in Operations Research & Management Science)
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. PRINT ON DEMAND. Nº de ref. del artículo: 18376515694
Gastos de envío:
EUR 9,95
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 4 disponibles
Imagen del vendedor
Data Science and Productivity Analytics
Publicado por
Springer International Publishing, Springer Nature Switzerland Mai 2020, 2020
ISBN 10: 3030433838
ISBN 13: 9783030433833
Nuevo
Tapa dura
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 452 pp. Englisch. Nº de ref. del artículo: 9783030433833
Gastos de envío:
EUR 60,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 2 disponibles
Imagen del vendedor
Data Science and Productivity Analytics
Publicado por
Springer International Publishing, 2020
ISBN 10: 3030433838
ISBN 13: 9783030433833
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783030433833
Gastos de envío:
EUR 64,20
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles