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


A good textbook on regression in statistics is the book Probability and Statistics by Mendenhall, Beaver, and Beaver, as mentioned in Chapter 6, Working with Data and Statistics. The following books provide a good introduction to classification and regression in modern data science:

  • James, G. and Witten, D., 2013. An Introduction To Statistical Learning: With Applications In R. New York: Springer.
  • Müller, A. and Guido, S., 2016. Introduction To Machine Learning With Python. Sebastopol: O'Reilly Media.

A good introduction to time series analysis can be found in the following book:

  • Cryer, J. and Chan, K., 2008. Time Series Analysis. New York: Springer.