£110.68

Springer Data Science and Productivity Analytics Book

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Description

Explore the intersection of modern data methods and efficiency with Data Science and Productivity Analytics. Published by Springer as part of the International Series in Operations Research and Management Science, this volume provides a comprehensive look at how scientific methods and systems extract knowledge from complex datasets. The text covers vital areas including productivity analysis, data envelopment analysis, and big data applications. It bridges the gap between theoretical concepts and practical applications by integrating mathematics, statistical methods, and operations research. Readers will find detailed coverage of how machine learning, computer programming, and pattern recognition contribute to the evolving field of data science. This book serves as a resource for understanding how to transform raw data into actionable insights. By combining econometrics with operations research, it offers a multi-domain perspective on managing performance and productivity in today's data-driven environment.

Key Features

Covers essential concepts in productivity analysis and data envelopment analysis for practical application.

Integrates data science and big data methods to help extract meaningful knowledge from large datasets.

Combines mathematics and statistical methods with operations research to provide a well-rounded technical foundation.

Explores the roles of machine learning and computer programming in modern data science processes.

Provides a deep look into econometrics and performance measurement within management science.

Part of the International Series in Operations Research and Management Science (Volume 290).

Product Specifications

Format
Paperback
Domain
Amazon UK
Release Date
23 May 2021
Listed Since
26 April 2021

Barcode

No barcode data available

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