Data Analysis and Related Applications 3: Theory and Practice, New Approaches
Author: Yiannis Dimotikalis (Editor), Christos H. Skiadas (Editor)
Publisher finelybook 出版社: Wiley-ISTE
Edition 版本: 1st
Publication Date 出版日期: 2024-05-29
Language 语言: English
Print Length 页数: 304 pages
ISBN-10: 1786309629
ISBN-13: 9781786309624
Book Description
The book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics. The chapters of this collaborative work represent a cross-section of current research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
The published data analysis methodology includes the updated state-of-the-art rapidly developed theory and applications of data expansion, both of which go through outstanding changes nowadays. New approaches are expected to deliver and have been developed, including Artificial Intelligence.
About the Author
Yiannis Dimotikalis is Assistant Professor of Quantitative Methods in the Department of Management Science and Technology at Hellenic Mediterranean University, Greece.
Christos H. Skiadas was the Founder and Director of Data Analysis and Forecasting and Former Vice-Rector at the Technical University of Crete, Greece. He is the Chair of the Applied Stochastic Models and Data Analysis conference series.
下载地址
相关推荐
- React Key Concepts: An in-depth guide to React’s core features, 2nd Edition
- Artificial Intelligence in Medicine and Healthcare
- Advanced Numerical Methods and Mathematical Modeling
- Hands-On AI Trading with Python, QuantConnect and AWS
- Mathematical Vignettes: Volume III: Non-Euclidean Geometry, Topology and Complex Analysis
- Artificial Intelligence for Future Networks