Mathematical Methods in Data Science

Erscheinungsjahr 2023
ISBN: 978-0-443-18679-0
Verlag: Elsevier Science & Technology

Buch, Englisch, 255 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 410 g

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 Based on the authors' recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations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

Autoren/Hrsg.

Ren, Jingli

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

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

Über Autor(innen)

Ren, Jingli
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.