Mathematical Methods in Data Science (Paperback)

Mathematical Methods in Data Science 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. The mathematics is accompanied with examples and problems arising in data science to demonstrate advanced mathematics, in particular, data-driven differential equations used. Chapters also cover network analysis, ordinary and partial differential equations based on recent published and unpublished results. Finally, the book 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. There are a number of books on mathematical methods in data science. Currently, all these related books primarily focus on linear algebra, optimization and statistical methods. However, network analysis, ordinary and partial differential equation models play an increasingly important role in data science. With the availability of unprecedented amount of clinical, epidemiological and social COVID-19 data, data-driven differential equation models have become more useful for infection prediction and analysis.

General

Imprint:	Elsevier - Health Sciences Division
Country of origin:	United States
Release date:	2023
First published:	2023
Authors:	Jingli Ren • Haiyan Wang
Dimensions:	229 x 152 x 16mm (L x W x T)
Format:	Paperback
Pages:	258
ISBN-13:	978-0-443-18679-0
Categories:	Books > Computing & IT > Applications of computing > Databases > Data capture & analysis
LSN:	0-443-18679-0
Barcode:	9780443186790

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