Book Image

The Statistics and Calculus with Python Workshop

By : Peter Farrell, Alvaro Fuentes, Ajinkya Sudhir Kolhe, Quan Nguyen, Alexander Joseph Sarver, Marios Tsatsos
5 (1)
Book Image

The Statistics and Calculus with Python Workshop

5 (1)
By: Peter Farrell, Alvaro Fuentes, Ajinkya Sudhir Kolhe, Quan Nguyen, Alexander Joseph Sarver, Marios Tsatsos

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.
Table of Contents (14 chapters)
Preface

Introduction

After going through a refresher on the Python language in the previous chapter, we are now ready to tackle the main topics of this book: mathematics and statistics.

Among others, the general fields of computational mathematics and statistics can be broken up into three main tool-centric components: representation and engineering; analysis and computation; and finally, visualization. In the ecosystem of the Python programming language, specific libraries are dedicated to each of these components (namely, pandas, NumPy, Matplotlib, and Seaborn), making the process modular.

While there might be other similar packages and tools, the libraries that we will be discussing have been proven to possess a wide range of functionalities and support powerful options in terms of computation, data processing, and visualization, making them some of a Python programmer's preferred tools over the years.

In this chapter, we will be introduced to each of these libraries and learn about their main API. Using a hands-on approach, we will see how these tools allow great freedom and flexibility in terms of creating, manipulating, analyzing, and visualizing data in Python. Knowing how to use these tools will also equip us for more complicated topics in the later chapters of this workshop.