The ability to retrieve and view tabular data has been covered multiple times in prior chapters; however, those examples were focused on the perspective of the consumer. We learned the skills necessary to understand what structured data is in, the many different forms it can take, and how to answer some questions from data. Our data literacy has increased during this time but we have relied on the producers of data sources to make it easier to read using a few Python commands or SQL commands. In this chapter, we are switching gears from being exclusively a consumer to now a producer of data by learning skills to manipulate data for analysis.
As a good data analyst, you will need both sides of the consumer and producer spectrum of skills to solve more complicated questions with data. For example, a common measure requested by businesses with web or mobile users is called usage analytics. This means counting the number of users...
