Book Image

Applying Math with Python

By : Sam Morley
Book Image

Applying Math with Python

By: Sam Morley

Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (12 chapters)

Further reading

Calculus is a very important part of every undergraduate mathematics course. There are a number of excellent textbooks on calculus, including the classic textbook by Spivak and the more comprehensive course by Adams and Essex:

  • Spivak, M. (2006). Calculus. 3rd ed. Cambridge: Cambridge University Press
  • Adams, R. and Essex, C. (2018). Calculus: A Complete Course. 9th ed. Don Mills, Ont: Pearson.Guassian

A good source for numerical differentiation and integration is the classic Numerical Recipes book, which gives a comprehensive description of how to solve many computational problems in C++, including a summary of the theory:

  • Press, W., Teukolsky, S., Vetterling, W. and Flannery, B. (2007). Numerical recipes: The Art of Scientific Computing. 3rd ed. Cambridge: Cambridge University Press