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000933070 019__ $$a1155706481$$a1156391637$$a1157264452
000933070 020__ $$a9783030433840$$q(electronic book)
000933070 020__ $$a3030433846$$q(electronic book)
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000933070 0247_ $$a10.1007/978-3-030-43384-0$$2doi
000933070 035__ $$aSP(OCoLC)on1156072906
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000933070 050_4 $$aQA276.12
000933070 08204 $$a519.5$$223
000933070 24500 $$aData science and productivity analytics /$$cVincent Charles, Juan Aparicio, Joe Zhu, editors.
000933070 260__ $$aCham :$$bSpringer,$$c2020.
000933070 300__ $$a1 online resource (441 pages).
000933070 336__ $$atext$$btxt$$2rdacontent
000933070 337__ $$acomputer$$bc$$2rdamedia
000933070 338__ $$aonline resource$$bcr$$2rdacarrier
000933070 347__ $$atext file$$bPDF$$2rda
000933070 4901_ $$aInternational Series in Operations Research and Management Science Ser. ;$$vv.290
000933070 506__ $$aAccess limited to authorized users.
000933070 520__ $$aThis book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of 'productivity analysis/data envelopment analysis' and 'data science/big data'. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data. Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.
000933070 588__ $$aDescription based on print version record.
000933070 650_0 $$aStatistics.
000933070 7001_ $$aCharles, Vincent.
000933070 7001_ $$aAparicio, Juan$$c(Associate professor of statistics and operations research)
000933070 7001_ $$aZhu, Joe,$$d1968-
000933070 77608 $$iPrint version:$$aCharles, Vincent$$tData Science and Productivity Analytics$$dCham : Springer,c2020$$z9783030433833
000933070 830_0 $$aInternational series in operations research & management science.
000933070 852__ $$bebk
000933070 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-43384-0$$zOnline Access$$91397441.1
000933070 909CO $$ooai:library.usi.edu:933070$$pGLOBAL_SET
000933070 980__ $$aEBOOK
000933070 980__ $$aBIB
000933070 982__ $$aEbook
000933070 983__ $$aOnline
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