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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

# Descriptive Statistics

As mentioned before, descriptive statistics and inferential statistics are the two main categories in the field of statistics. With descriptive statistics, our goal is to compute specific quantities that can convey important information about—or in other words, describe—our data.

From within descriptive statistics, there are two main subcategories: central tendency statistics and dispersion statistics. The actual terms are suggestive of their respective meaning: central tendency statistics are responsible for describing the center of the distribution of the given data, while dispersion statistics convey information about the spread or range of the data away from its center.

One of the clearest examples of this distinction is from the familiar normal distribution, whose statistics include a mean and a standard deviation. The mean, which is calculated to be the average of all the values from the probability distribution, is suitable for estimating...