5 (1)

5 (1)

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

Summary

In this chapter, we learned about the first steps toward performing any kind of statistical analysis: first, we defined our business problem and introduced the dataset. Based on the problem we wanted to solve, we prepared the dataset accordingly: we deleted some records, imputed missing values, transformed the types of some variables, and created new ones. Then we learned about the need for descriptive statistics; we learned how easy it is to calculate them using pandas and how to use and interpret those calculations. In the final section, we learned about how we can combine visualizations with descriptive statistics to get a deeper understanding of the relationships between variables in our datasets. What we learned in this chapter are concepts and techniques that you will be able to put in practice in any data analysis you perform. However, to get more sophisticated in your analysis, you need to have a good grasp of the basics of probability theory, which is the subject of...