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Key benefits

Compute complex mathematical problems using programming logic with the help of step-by-step recipes

Learn how to use Python libraries for computation, mathematical modeling, and statistics

Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics

Description

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore 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.

Who is this book for?

Whether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.

What you will learn

Become familiar with basic Python packages, tools, and libraries for solving mathematical problems

Explore real-world applications of mathematics to reduce a problem in optimization

Understand the core concepts of applied mathematics and their application in computer science

Find out how to choose the most suitable package, tool, or technique to solve a problem

Implement basic mathematical plotting, change plot styles, and add labels to plots using Matplotlib

Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

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Amazon verified reviews

Yiqiao Yin Jan 28, 2023

I really enjoy reading this. The book reboot my knowledge in several core mathematical fields. It provides a whole package in mathematics, numpy, scipy, and matplotlib. As I progress in learning the materials, I gradually understand the topics in the book with regards to calculus, probability, and network and so on. It's a highly engineering and physics-based title. The chapters took me through a collection of miscellaneous problems with data formats and coding walkthrough. I highly recommend it!

Amazon Verified review

Akul Apr 19, 2023

I really liked reading this title. The book helped me review my knowledge in complex as well as foundational mathematical topics with its application in Python. I’ll recommend this book for people wanting to get a refresher on mathematical concepts applicable in Machine Learning/Data Science which in turn will help improve their conceptual understanding of the model’s black box. A must read indeed!!

H2N Nov 16, 2023

This book is an indispensable guide for Python enthusiasts tackling mathematical challenges. It begins with foundational Python packages, including NumPy and SciPy, then advances to Matplotlib for essential plotting skills. The chapters progress through calculus, differential equations, and randomness and probability basics, providing a solid mathematical base. It explores practical Python applications in areas such as trees, networks, and data analysis, and includes regression, forecasting, and geometric problem-solving. Additionally, it introduces optimization and game theory, and concludes with valuable productivity tips for Python in mathematical problem-solving, making it a comprehensive resource for varied mathematical pursuits with Python.

Dror Feb 11, 2023

Python has become the programming language of choice for computation in most science and engineering disciplines, including math, physics, data science and many others. This is largely due to its vast ecosystem of mathematical and scientific packages, but this large ecosystem can also be overwhelming for many students and beginners.This highly practical book provides a gentle introduction to using Python for solving problems in math and science. It begins with a clear and useful overview of the fundamental Python packages for scientific computing, such as NumPy, SciPy and Matplotlib. It then progresses to describe how to solve a diverse collection of problems in math and science using Python, in areas such as data analysis and statistics, networks, regression and forecasting, optimization, and game theory. These descriptions are clear and concise, and gradually present additional common and helpful Python packages for scientific programming.I would highly recommend this book to anyone interested in learning how to use Python for scientific computing and data analysis. It requires no more than basic knowledge of the Python programming language, and will be ideal for students in math, science and engineering who are taking their first steps in the world of scientific programming. Experienced software engineers and other Python programmers looking to expand their knowledge into scientific computing and data analysis will also highly benefit from reading this wonderful and rather unique book.

Steven Fernandes Mar 09, 2023

The updated Applying Math with Python edition offers efficient solutions for complex problems in various mathematical fields. It includes revised old recipes and new ones to showcase tools such as JAX. You'll refresh core mathematical concepts and learn about packages in Python's scientific stack. Gradually, you'll tackle advanced topics like calculus, probability, and networks, leading to exploring Python's applications in data science, statistics, forecasting, geometry, and optimization. The book concludes with miscellaneous problems, including data formats and code acceleration. You'll gain practical coding solutions to solve various computational math and data science problems.

Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems , Second Edition

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