#### Overview of this book

Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.
Table of Contents (14 chapters)
Preface
1. Fundamentals of Python
Free Chapter
2. Python's Main Tools for Statistics
3. Python's Statistical Toolbox
4. Functions and Algebra with Python
5. More Mathematics with Python
6. Matrices and Markov Chains with Python
7. Doing Basic Statistics with Python
8. Foundational Probability Concepts and Their Applications
9. Intermediate Statistics with Python
10. Foundational Calculus with Python
11. More Calculus with Python
12. Intermediate Calculus with Python

# Introduction

So far, we have learned how to use the Python language, especially three of its core libraries—NumPy, pandas, and Matplotlib, for statistics and data science. However, in order to fully take advantage of these tools, we will need to have a solid theoretical understanding of statistics itself. By knowing the idea behind statistical tests and techniques, we will be able to utilize the tools that Python offers more effectively.

It is true that in statistics and machine learning, libraries in Python offer great options—from data cleaning/processing to modeling and making inferences. However, a fundamental understanding of statistics is still required so that we can make initial decisions regarding what kinds of techniques should be used in our process, depending on the data we have.

As such, in this chapter, we will learn about core concepts in statistics such as , inference, sampling, variables, and so on. We will also be introduced to a wide range of...