Mathematical Methods In Data Science Jingli Ren Haiyan Wang by Jingli Ren, Haiyan Wang 9780443186790, 0443186790 instant download after payment.

There are a number of books on mathematical methods in data science. Currently, all of these related books primarily focus on linear algebra, optimization, and statistical methods. However, ordinary and partial differential equation models play an increasingly important role in data science. For example, ordinary differential equation models, in particular, SIR (Susceptible-Infected-Recovered) models, have been extensively used for infectious disease modeling and prediction. With the availability of an unprecedented amount of clinical, epidemiological, and social COVID-19 data, data-driven differential equation models have revealed new insights into the spread and control of COVID-19.
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 The material in Chapters 4, 5, and 6 are based on the two authors’ published and unpublished works on analysis and prediction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