Data Science and Productivity Analytics

This book provides a comprehensive overview of data science and its applications in productivity analysis and data envelopment analysis,including techniques such as linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modeling, deep learning, text analysis, survival analysis, and more. It aims to help researchers and practitioners unlock the value and drive productivity from large data sets.

Format: Paperback / softback
Length: 439 pages
Publication date: 23 May 2021
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into a wide range of concepts, including performance, productivity, operations research, econometrics, and data science, with a particular focus on the practical and theoretical aspects of productivity analysis/data envelopment analysis and data science/big data. Data science, as defined, encompasses a collection of scientific methods, processes, and systems dedicated to extracting valuable knowledge or insights from data. It builds upon principles from various domains, such as mathematics, statistics, operations research, machine learning, computer programming, pattern recognition, and data visualization, among others.

Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifiers, principal component analysis, neural networks, predictive modeling, deep learning, text analysis, survival analysis, and many more. These techniques empower users to make informed decisions by leveraging the power of data.

However, it is undeniable that the volume of data is rapidly expanding, and analyzing large data sets has become a critical factor in competitiveness and innovation, driving new waves of productivity growth. This book seeks to provide a fresh perspective on how data science techniques can unlock value and drive productivity from the vast mountains of data.

Researchers working in productivity analysis/data envelopment analysis will greatly benefit from gaining insights into the tools and techniques available in data science/big data that can enhance their current research analyses and endeavors. On the other hand, data scientists will also find value in exploring the numerous applications of productivity analysis/data envelopment analysis.

By leveraging the combined expertise of these two fields, this book aims to bridge the gap between data science and productivity analysis, fostering collaboration and driving innovation in the realm of data-driven decision-making.

Weight: 688g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030433864
Edition number: 1st ed. 2020