Mathematical Methods in Data Science

Mathematical Methods in Data Science is a book that introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. It includes examples and problems from data science and explains advanced mathematical concepts, making it accessible to researchers and graduate students in mathematics and data science.

Format: Paperback / softback
Length: 258 pages
Publication date: 11 January 2023
Publisher: Elsevier - Health Sciences Division

Mathematical Methods in Data Science is a comprehensive guide that delves into the diverse array of mathematical tools employed in the field of data science. These tools encompass a wide range of disciplines, including calculus, linear algebra, optimization, network analysis, probability, and differential equations. Drawing upon the authors' recent and previously unpublished research, this book presents a novel approach rooted in network analysis, enabling the integration of large datasets into the framework of ordinary and partial differential equations for data analysis and prediction. Given the pervasive application of data science across various societal domains, the book includes illustrative examples and problem-solving scenarios drawn from the realm of data science, accompanied by lucid explanations of advanced mathematical concepts, particularly data-driven differential equations. This makes the text accessible to both researchers and graduate students with backgrounds in mathematics and data science, fostering a deeper understanding of the mathematical foundations underlying data-driven approaches.

Weight: 418g
Dimension: 227 x 151 x 16 (mm)
ISBN-13: 9780443186790