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20240909213021.0 |
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|a 1155706481
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|a 1162658108
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|q (electronic bk.)
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|a 3030433846
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|2 doi
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|a com.springer.onix.9783030433840
|b Springer Nature
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|a QA276.12
|b .D28 2020eb
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|a HCDD
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|a Data science and productivity analytics /
|c Vincent Charles, Juan Aparicio, Joe Zhu, editors.
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|a Cham :
|b Springer,
|c 2020.
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|c ©2020
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| 300 |
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|a 1 online resource (x, 439 pages)
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|a text
|b txt
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|a online resource
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|a text file
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|b PDF
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|a International Series in Operations Research and Management Science Ser. ;
|v v. 290
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|a Print version record.
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| 505 |
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|t Data Envelopment Analysis and Big Data: Revisit with a Faster Method /
|r Dariush Khezrimotlagh, Joe Zhu --
|t Data Envelopment Analysis (DEA): Algorithms, Computations, and Geometry /
|r José H. Dulá --
|t An Introduction to Data Science and Its Applications /
|r Alex Rabasa, Ciara Heavin --
|t Identification of Congestion in DEA /
|r Mahmood Mehdiloo, Biresh K. Sahoo, Joe Zhu --
|t Data Envelopment Analysis and Non-parametric Analysis /
|r José H. Dulá --
|t An Introduction to Data Science and Its Applications /
|r Gabriel Villa, Sebastián Lozano --
|t The Measurement of Firms' Efficiency Using Parametric Techniques /
|r Luis Orea --
|t Fair Target Setting for Intermediate Products in Two-Stage Systems with Data Envelopment Analysis /
|r Qingxian An, Haoxun Chen, Beibei Xiong, Jie Wu, Liang Liang --
|t Fixed Cost and Resource Allocation Considering Technology Heterogeneity in Two-Stage Network Production Systems /
|r Tao Ding, Feng Li, Liang Liang --
|t Efficiency Assessment of Schools Operating in Heterogeneous Contexts: A Robust Nonparametric Analysis Using PISA 2015 /
|r Jose Manuel Cordero, Cristina Polo, Rosa Simancas --
|t A DEA Analysis in Latin American Ports: Measuring the Performance of Guayaquil Contecon Port /
|r Emilio J. Morales-Núñez, Xavier R. Seminario-Vergara, Sonia Valeria Avilés-Sacoto, Galo Eduardo Mosquera-Recalde --
|t Effects of Locus of Control on Bank's Policy--A Case Study of a Chinese State-Owned Bank /
|r Cong Xu, Guo-liang Yang, Jian-bo Yang, Yu-wang Chen, Hua-ying Zhu --
|t A data scientific approach to measure hospital productivity /
|r Babak Daneshvar Rouyendegh (B. Erdebilli), Asil Oztekin, Joseph Ekong, Ali Dag --
|t Environmental Application of Carbon Abatement Allocation by Data Envelopment Analysis /
|r Anyu Yu, Simon Rudkin, Jianxin You --
|t Pension Funds and Mutual Funds Performance Measurement with a New DEA (MV-DEA) Model Allowing for Missing Variables /
|r Maryam Badrizadeh, Joseph C. Paradi --
|t Sharpe Portfolio Using a Cross-Efficiency Evaluation /
|r Mercedes Landete, Juan F. Monge, José L. Ruiz, José V. Segura.
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| 520 |
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|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, 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.
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| 650 |
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|a Statistics.
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| 650 |
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|a Labor productivity.
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| 650 |
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|a Productivity accounting.
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| 650 |
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7 |
|a statistics.
|2 aat
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| 650 |
|
7 |
|a Economic theory & philosophy.
|2 bicssc
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| 650 |
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7 |
|a Probability & statistics.
|2 bicssc
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| 650 |
|
7 |
|a Operational research.
|2 bicssc
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| 650 |
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7 |
|a Business & Economics
|x Economics
|x Theory.
|2 bisacsh
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| 650 |
|
7 |
|a Mathematics
|x Probability & Statistics
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Business & Economics
|x Operations Research.
|2 bisacsh
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| 650 |
|
7 |
|a Productivity accounting
|2 fast
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| 650 |
|
7 |
|a Labor productivity
|2 fast
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| 650 |
|
7 |
|a Statistics
|2 fast
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| 655 |
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0 |
|a Electronic books.
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| 700 |
1 |
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|a Charles, Vincent.
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| 700 |
1 |
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|a Aparicio, Juan
|c (Associate professor of statistics and operations research)
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| 700 |
1 |
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|a Zhu, Joe,
|d 1968-
|1 https://id.oclc.org/worldcat/entity/E39PCjKm4DVhCHkD7RgvBghHQ3
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| 776 |
0 |
8 |
|i Print version:
|a Charles, Vincent.
|t Data Science and Productivity Analytics.
|d Cham : Springer, ©2020
|z 9783030433833
|
| 830 |
|
0 |
|a International series in operations research & management science.
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| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-43384-0
|y Click for online access
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|a SPRING-BUS2020
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| 994 |
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|a 92
|b HCD
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