LEADER 05625nam 22006255 450 001 9910484237403321 005 20200702122227.0 010 $a3-030-43384-6 024 7 $a10.1007/978-3-030-43384-0 035 $a(CKB)4100000011263824 035 $a(MiAaPQ)EBC6207827 035 $a(DE-He213)978-3-030-43384-0 035 $a(PPN)248394800 035 $a(EXLCZ)994100000011263824 100 $a20200522d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Science and Productivity Analytics /$fedited by Vincent Charles, Juan Aparicio, Joe Zhu 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (441 pages) 225 1 $aInternational Series in Operations Research & Management Science,$x0884-8289 ;$v290 311 $a3-030-43383-8 320 $aIncludes bibliographical references. 327 $aChapter 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. 330 $aThis book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ?productivity analysis/data envelopment analysis? and ?data science/big data?. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naļve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data. Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis. 410 0$aInternational Series in Operations Research & Management Science,$x0884-8289 ;$v290 606 $aOperations research 606 $aDecision making 606 $aEconomic theory 606 $aStatistics  606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 606 $aEconomic Theory/Quantitative Economics/Mathematical Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/W29000 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 615 0$aOperations research. 615 0$aDecision making. 615 0$aEconomic theory. 615 0$aStatistics . 615 14$aOperations Research/Decision Theory. 615 24$aEconomic Theory/Quantitative Economics/Mathematical Methods. 615 24$aStatistical Theory and Methods. 676 $a658.4034 702 $aCharles$b Vincent$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAparicio$b Juan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhu$b Joe$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484237403321 996 $aData Science and Productivity Analytics$92844612 997 $aUNINA