Mathematical Methods in Data Science

Erscheinungsjahr 2023
ISBN: 978-0-443-18679-0
Verlag: Elsevier Inc

Buch, Englisch, 255 Seiten, Format (B × H): 153 mm x 230 mm, Gewicht: 421 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 Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations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

Wang, Haiyan

Fachgebiete

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

Inhaltsverzeichnis

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