Mathematical Methods In Data Science 1st Jingli Ren Haiyan Wang by Jingli Ren, Haiyan Wang 9780443186806, 0443186804 instant download after payment.

In this book, we will cover a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability, and differential equations. In particular, 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. The techniques in linear algebra, probability, calculus and optimization, and network analysis in Chapters 1, 2, 3, 4 are necessary for understanding the applications of differential equations in data science. For example, eigenvalues are used in network clustering, and gradient descent is extensively used in the training of differential equations for various predictions. The material in Chapters 4, 5, and 6 are based on the two authors’ published and unpublished works on analysis and prediction with data-driven ordinary and partial differential equations.
Data science is virtually used in every section in our society. This timely book is of great interest to a broad range of readers including advanced undergraduate students, graduate students, and researchers. Background preparations and necessary references are also included to ensure the book is accessible to general readers who are interested in data science.