Mathematical Methods in Data Science

Jingli Ren, Haiyan Wang

• Publisher: Elsevier
• Year: 2023
• ISBN:
  9780443186806
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

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 Based on the authors' recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations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 dataanalysis 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.

• Combines a broad spectrum of mathematics, including linear algebra, optimization, network analysis and ordinary and partial differential equations for data science
• Written by two researchers who are actively applying mathematical and statistical methods as well as ODE and PDE for data analysis and prediction
• Highly interdisciplinary, with content spanning mathematics, data science, social media analysis, network science, financial markets, and more
• Presents a wide spectrum of topics in a logical order, including probability, linear algebra, calculus and optimization, networks, ordinary differential and partial differential equations

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• Acknowledgments
• Chapter 1: Linear algebra
• Chapter 2: Probability
• Chapter 3: Calculus and optimization
• Chapter 4: Network analysis
• Chapter 5: Ordinary differential equations
• Chapter 6: Partial differential equations
• Bibliography
• Index