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Can I read Mathematical Methods in Data Science on EtoBox?
Mathematical Methods in Data Science by Jingli Ren; Haiyan Wang is a nonfiction available to read on EtoBox.
What is Mathematical Methods in Data Science about?
There are a number of books on mathematical methods in data science. Currently, all of these related books primarily focus on linear algebra, optimization, and statistical methods. However, ordinary and partial differential equation models play an increasingly important role in data science. For example, ordinary differential equation models, in particular, SIR (Susceptible-Infected-Recovered) models, have been extensively used for infectious disease modeling and prediction. With the availability of an unprecedented amount of clinical, epidemiological, and social COVID-19 data, data-driven differential equation models have revealed new insights into the spread and control of COVID-19. In this book, we will cover a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability, and differential equations. In particular, the book introducesa new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. The techniques in linear algebra, probability, calculusand optimization, and network analysis in Chapters 1, 2, 3,
Who reads Mathematical Methods in Data Science?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Jingli Ren; Haiyan Wang
- Publisher
- Elsevier - Health Sciences Division
- Published
- 2023
- Language
- EN
- ISBN
- 9780443186806
- Category
- nonfiction
- Subjects
- Mathematics, Science, Computer Science