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Mathematical Methods in Data Science
- 1st Edition - January 6, 2023
- Authors: Jingli Ren, Haiyan Wang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 8 6 7 9 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 8 6 8 0 - 6
Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probabili… Read more
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Request a sales quoteMathematical 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 Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations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.
- 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
Advanced undergraduate students, graduate students and researchers. Background preparations and necessary references
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Acknowledgments
- Chapter 1: Linear algebra
- Abstract
- 1.1. Introduction
- 1.2. Elements of linear algebra
- 1.3. Linear regression
- 1.4. Principal component analysis
- Bibliography
- Chapter 2: Probability
- Abstract
- 2.1. Introduction
- 2.2. Probability distribution
- 2.3. Independent variables and random samples
- 2.4. Maximum likelihood estimation
- Bibliography
- Chapter 3: Calculus and optimization
- Abstract
- 3.1. Introduction
- 3.2. Continuity and differentiation
- 3.3. Unconstrained optimization
- 3.4. Logistic regression
- 3.5. K-means
- 3.6. Support vector machine
- 3.7. Neural networks
- Bibliography
- Chapter 4: Network analysis
- Abstract
- 4.1. Introduction
- 4.2. Graph modeling
- 4.3. Spectral graph bipartitioning
- 4.4. Network embedding
- 4.5. Network based influenza prediction
- Bibliography
- Chapter 5: Ordinary differential equations
- Abstract
- 5.1. Introduction
- 5.2. Basic differential equation models
- 5.3. Prediction of daily PM2.5 concentration
- 5.4. Analysis of COVID-19
- 5.5. Analysis of COVID-19 in Arizona
- Bibliography
- Chapter 6: Partial differential equations
- Abstract
- 6.1. Introduction
- 6.2. Formulation of partial differential equation models
- 6.3. Bitcoin price prediction
- 6.4. Prediction of PM2.5 in China
- 6.5. Prediction of COVID-19 in Arizona
- 6.6. Compliance with COVID-19 mitigation policies in the US
- Bibliography
- Bibliography
- Bibliography
- Index
- No. of pages: 258
- Language: English
- Edition: 1
- Published: January 6, 2023
- Imprint: Elsevier
- Paperback ISBN: 9780443186790
- eBook ISBN: 9780443186806
JR
Jingli Ren
She received the Ph.D. degree in applied mathematics from Beijing Institute of Technology, Beijing, China, in 2004. Her research interests include data science, applied mathematics, and applied statistics. She conducted five Projects of National Nature Science Foundation of China, one Alexander von Humboldt Fellowship for Experienced Researcher, and five Provincial Projects. She has published numerous articles in scholarly journals, such as Acta Mater.、Appl. Phys. Lett.、IEEE Trans. SMC、Infor. Sci.、J. Stat. Phys.、J. Nonlinear Sci.、 Phys. Rev. B、Phys. Rev. E、Sci. China Math.、Sci. China Phys. and Sci. China Mater., etc.
Affiliations and expertise
Professor, Zhengzhou University, ChinaHW
Haiyan Wang
He completed his doctorate in mathematics, while also earning a master's degree in computer science at Michigan State University in 1997. He worked as a full-time software engineer in industry for almost ten years before joining Arizona State University. Dr. Wang’s research interests include applied mathematics, data science, differential equations, online social networks. He has published numerous articles in scholarly journals and a book entitled, “Modeling Information Diffusion in Online Social Networks with Partial Differential Equations”, Springer, 2020. Recently he developed and taught a course, Mathematical Methods in Data Science, at Arizona State University.
Affiliations and expertise
Arizona State University, USARead Mathematical Methods in Data Science on ScienceDirect