Data rarely arrives in a format ready for analysis. Before you can derive insights, you first need to acquire the data and then prepare it. This chapter focuses on these fundamental first steps in the data science workflow.

You will learn practical approaches for locating and obtaining data from common sources. We will cover the essential concepts of data cleaning, including strategies for dealing with frequent issues like missing values and identifying unusual data points known as outliers. Additionally, we'll touch upon basic data transformations needed to make data consistent and usable for analysis tools. The chapter includes a practical exercise to reinforce the concepts of loading and inspecting a dataset.