東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data science and productivity analytics

Charles, Vincent.

FindBook

Google Book

Amazon

博客來

Data science and productivity analytics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science and productivity analytics/ edited by Vincent Charles, Juan Aparicio, Joe Zhu.
其他作者:	Charles, Vincent.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	x, 439 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Data Envelopment Analysis and Big Data: Revisit with a Faster Method -- Chapter 2. Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry -- Chapter 3. An Introduction to Data Science and Its Applications: an Introduction to Data Science and Its Applications -- Chapter 4. Identification of Congestion in DEA -- Chapter 5. Data Envelopment Analysis and Non-Parametric Analysis -- Chapter 6. The Measurement of Firms' Efficiency Using Parametric Techniques -- Chapter 7. Fair Target Setting for Intermediate Products in Two-Stage Systems With Data Envelopment Analysis -- Chapter 8. Fixed Cost and Resource Allocation Considering Technology Heterogeneity in Two-Stage Network Production Systems -- Chapter 9.Efficiency Assessment of Schools Operating in Heterogeneous Contexts: A Robust Nonparametric Analysis Using Pisa 2015 -- Chapter 10. A DEA Analysis in Latin-American Ports: Measuring the Performance of Guayaquil Contecon Port -- Chapter 11. Effects of Locus of Control on Bank's Policy - a Case Study of a Chinese State Owned Bank -- Chapter 12. A Data Scientific Approach to Measure Hospital Productivity -- Chapter 13. Environmental Application of Carbon Abatement Allocation by Data Envelopment Analysis -- Chapter 14. Pension Funds and Mutual Funds Performance Measurement With a New DEA (Mv-DEA) Model Allowing for Missing Variables -- Chapter 15. Sharpe Portfolio Using a Cross-Efficiency Evaluation.
Contained By:	Springer eBooks
標題:	Statistics. -
電子資源:	https://doi.org/10.1007/978-3-030-43384-0
ISBN:	9783030433840

Data science and productivity analytics
Data science and productivity analytics[electronic resource] /edited by Vincent Charles, Juan Aparicio, Joe Zhu. - Cham :Springer International Publishing :2020. - x, 439 p. :ill., digital ;24 cm. - International series in operations research & management science,2900884-8289 ;. - International series in operations research & management science ;290..

Chapter 1. Data Envelopment Analysis and Big Data: Revisit with a Faster Method -- Chapter 2. Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry -- Chapter 3. An Introduction to Data Science and Its Applications: an Introduction to Data Science and Its Applications -- Chapter 4. Identification of Congestion in DEA -- Chapter 5. Data Envelopment Analysis and Non-Parametric Analysis -- Chapter 6. The Measurement of Firms' Efficiency Using Parametric Techniques -- Chapter 7. Fair Target Setting for Intermediate Products in Two-Stage Systems With Data Envelopment Analysis -- Chapter 8. Fixed Cost and Resource Allocation Considering Technology Heterogeneity in Two-Stage Network Production Systems -- Chapter 9.Efficiency Assessment of Schools Operating in Heterogeneous Contexts: A Robust Nonparametric Analysis Using Pisa 2015 -- Chapter 10. A DEA Analysis in Latin-American Ports: Measuring the Performance of Guayaquil Contecon Port -- Chapter 11. Effects of Locus of Control on Bank's Policy - a Case Study of a Chinese State Owned Bank -- Chapter 12. A Data Scientific Approach to Measure Hospital Productivity -- Chapter 13. Environmental Application of Carbon Abatement Allocation by Data Envelopment Analysis -- Chapter 14. Pension Funds and Mutual Funds Performance Measurement With a New DEA (Mv-DEA) Model Allowing for Missing Variables -- Chapter 15. Sharpe Portfolio Using a Cross-Efficiency Evaluation.

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, Naive 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.

ISBN: 9783030433840

Standard No.: 10.1007/978-3-030-43384-0doiSubjects--Topical Terms:

517247
Statistics.

LC Class. No.: QA276.12 / .D383 2020

Dewey Class. No.: 519.5

Data science and productivity analytics
LDR:04286nmm a2200349 a 4500 001 2254923
003 DE-He213
005 20200922154501.0
006 m d
007 cr nn 008maaau
008 220419s2020 sz s 0 eng d
020 $a 9783030433840 $q (electronic bk.)
020 $a 9783030433833 $q (paper)
024 7 $a 10.1007/978-3-030-43384-0 $2 doi
035 $a 978-3-030-43384-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.12 $b .D383 2020
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276.12 $b .D232 2020
245 0 0 $a Data science and productivity analytics $h [electronic resource] / $c edited by Vincent Charles, Juan Aparicio, Joe Zhu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a x, 439 p. : $b ill., digital ; $c 24 cm.
490 1 $a International series in operations research & management science, $x 0884-8289 ; $v 290
505 0 $a Chapter 1. Data Envelopment Analysis and Big Data: Revisit with a Faster Method -- Chapter 2. Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry -- Chapter 3. An Introduction to Data Science and Its Applications: an Introduction to Data Science and Its Applications -- Chapter 4. Identification of Congestion in DEA -- Chapter 5. Data Envelopment Analysis and Non-Parametric Analysis -- Chapter 6. The Measurement of Firms' Efficiency Using Parametric Techniques -- Chapter 7. Fair Target Setting for Intermediate Products in Two-Stage Systems With Data Envelopment Analysis -- Chapter 8. Fixed Cost and Resource Allocation Considering Technology Heterogeneity in Two-Stage Network Production Systems -- Chapter 9.Efficiency Assessment of Schools Operating in Heterogeneous Contexts: A Robust Nonparametric Analysis Using Pisa 2015 -- Chapter 10. A DEA Analysis in Latin-American Ports: Measuring the Performance of Guayaquil Contecon Port -- Chapter 11. Effects of Locus of Control on Bank's Policy - a Case Study of a Chinese State Owned Bank -- Chapter 12. A Data Scientific Approach to Measure Hospital Productivity -- Chapter 13. Environmental Application of Carbon Abatement Allocation by Data Envelopment Analysis -- Chapter 14. Pension Funds and Mutual Funds Performance Measurement With a New DEA (Mv-DEA) Model Allowing for Missing Variables -- Chapter 15. Sharpe Portfolio Using a Cross-Efficiency Evaluation.
520 $a 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, Naive 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.
650 0 $a Statistics. $3 517247
650 1 4 $a Operations Research/Decision Theory. $3 890895
650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 2162305
650 2 4 $a Statistical Theory and Methods. $3 891074
700 1 $a Charles, Vincent. $3 3378931
700 1 $a Aparicio, Juan. $3 3205986
700 1 $a Zhu, Joe. $3 895559
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a International series in operations research & management science ; $v 290. $3 3524180
856 4 0 $u https://doi.org/10.1007/978-3-030-43384-0
950 $a Business and Management (Springer-41169)