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#### 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

# Python's Other Statistics Tools

In the previous chapter, we considered Python's three main libraries, which make up the majority of a common data science/scientific computing pipeline: NumPy for multi-dimensional matrix computation, pandas for tabular data manipulation, and Matplotlib for data visualization.

Along the way, we have also discussed a number of supporting tools that complement those three libraries well; they are seaborn for the implementation of complex visualizations, SciPy for statistical and scientific computing capability, and scikit-learn for advanced data analysis needs.

Needless to say, there are also other tools and libraries that, even though they did not fit into our discussions well, offer unique and powerful capabilities for particular tasks in scientific computing. In this section, we will briefly consider some of them so that we can gain a comprehensive understanding of what Python tools are available for which specific tasks.

These tools...