Data science and productivity analytics

Vincent Charles (Editor), Juan Aparicio (Editor), Joe Zhu (Editor)

Research output: Book/ReportBook

Abstract

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.
Original languageEnglish
Place of PublicationCham
PublisherSpringer Cham
Number of pages439
Volume290
Edition1
ISBN (Electronic)978-3-030-43386-4
ISBN (Print)978-3-030-43383-3
DOIs
Publication statusPublished - 23 May 2020
Externally publishedYes

Publication series

NameInternational Series in Operations Research & Management Science
PublisherSpringer Cham
ISSN (Print)0884-8289
ISSN (Electronic)2214-7934

Keywords

  • Artificial Intelligence
  • Data Science
  • Data Envelopment Analysis (DEA)
  • Big Data
  • Efficiency
  • Parametric Analytics
  • Combinatory Analytics
  • Multidimensional Scaling
  • Data Visualization
  • Productivity Analysis

ASJC Scopus subject areas

  • General Decision Sciences

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