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.
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