Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications

This book investigates the role of data mining in computational statistics for machine learning, offering applications in various domains and examining the role of transformation functions in optimizing problem statements. It is a valuable resource for students, professionals, and researchers in data mining, computational statistics, operations research, and machine learning.

Format: Hardback
Length: 246 pages
Publication date: 25 March 2022
Publisher: Taylor & Francis Ltd

The rapidly evolving fields of Data Analytics and Computational Statistics demand a keen awareness of current trends, methodologies, and applications. This comprehensive book delves into the crucial role of data mining in computational statistics for machine learning. It presents a wide range of applications that can be applied across various domains, while also examining the significance of transformation functions in optimizing problem statements.

Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications offers a comprehensive exploration of computationally intensive methods, inference techniques, and survival analysis models. It sheds light on how data mining extracts valuable information and how machine learning enhances computational models through the integration of new insights.

This reference work is of immense interest to students, professionals, and researchers engaged in the fields of data mining, computational statistics, operations research, and machine learning. It serves as a valuable resource for advancing knowledge and understanding in these dynamic domains.

Dimension: 234 x 156 (mm)
ISBN-13: 9780367715113