#### 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.
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

In the previous chapter, we learned how to perform the first steps in any statistical analysis. Given a business or scientific problem and a related dataset, we learned how to load the dataset and prepare it for analysis. Then, we learned how to calculate and use descriptive statistics to make sense of the variables. Finally, we performed EDA to complement the information we gathered from the descriptive statistics and gained a better understanding of the variables and their possible relationships. After getting a basic understanding of an analytical problem, you may need to go one step further and use more sophisticated quantitative tools, some of which are used in the following fields:

• Inferential statistics
• Machine learning
• Prescriptive analytics
• Optimization

What do all of these domains have in common? Many things: for example, they have a mathematical nature, they make heavy use of computational tools, and in one way or another they use...