*Chapter 1*: Introduction to Data Analysis

Before we can begin our hands-on introduction to data analysis with `pandas`

, we need to learn about the fundamentals of data analysis. Those who have ever looked at the documentation for a software library know how overwhelming it can be if you have no clue what you are looking for. Therefore, it is essential that we master not only the coding aspect but also the thought process and workflow required to analyze data, which will prove the most useful in augmenting our skill set in the future.

Much like the scientific method, data science has some common workflows that we can follow when we want to conduct an analysis and present the results. The backbone of this process is **statistics**, which gives us ways to describe our data, make predictions, and also draw conclusions about it. Since prior knowledge of statistics is not a prerequisite, this chapter will give us exposure to the statistical concepts we will use throughout this book, as well as areas for further exploration.

After covering the fundamentals, we will get our Python environment set up for the remainder of this book. Python is a powerful language, and its uses go way beyond data science: building web applications, software, and web scraping, to name a few. In order to work effectively across projects, we need to learn how to make **virtual environments**, which will isolate each project's dependencies. Finally, we will learn how to work with Jupyter Notebooks in order to follow along with the text.

The following topics will be covered in this chapter:

- The fundamentals of data analysis
- Statistical foundations
- Setting up a virtual environment