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

There are a large number of textbooks on statistics and statistical theory. The following book was used as reference for this chapter:

  • Mendenhall, W., Beaver, R., and Beaver, B., (2006), Introduction To Probability And Statistics, 12th ed., (Belmont, Calif.: Thomson Brooks/Cole)

The pandas documentation ( and the following pandas book serve as good references for working with pandas:

  • McKinney, W.,(2017),Python for Data Analysis, 2nd ed.,(Sebastopol: O'Reilly Media, Inc, US)

The SciPy documentation ( also contains detailed information about the statistics module that was used several times in this chapter.