Mathematical Methods in Data Science

Introduces a new approach to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction

Jingli Ren (Author), Haiyan Wang (Author)

9780443186790, Elsevier Science

Paperback / softback, published 11 January 2023

258 pages
22.9 x 15.2 x 1.7 cm, 0.41 kg

Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations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. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

1. Linear Algebra 2. Probability 3. Calculus and Optimization 4. Network Analysis 5. Ordinary Differential Equations 6. Partial Differential Equations

Subject Areas: Information technology: general issues [UB]