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

Types of Data in Statistics

In statistics, there are two main types of data: categorical data and numerical data. Depending on which type an attribute or a variable in your dataset belongs to, its data processing, modeling, analysis, and visualization techniques might differ. In this section, we will explain the details of these two main data types and discuss relevant points for each of them, which are summarized in the following table:

Figure 3.1: Data type comparison

For the rest of this section, we will go into more detail about each of the preceding comparisons, starting with categorical data in the next subsection.

Categorical Data

When an attribute or a variable is categorical, the possible values it can take belong to a predetermined and fixed set of values. For example, in a weather-related dataset, you might have an attribute to describe the overall weather for each day, in which case that attribute might be among a list of discrete values such...