This section provides an overview of the most common data types that data scientists must deal with on a regular basis and some of the variations between these types. We also talk about converting between data types and how to safely convert without losing information (or at least understanding the risks beforehand).

This section also covers the mysterious world of empties, nulls, and blanks. We explore the various types of missing data and describe how missing data can negatively affect results of data analysis. We will compare choices and trade-offs for handling the missing data and some of the pros and cons of each method.

As much of our data will be stored as strings, we will learn to identify different character encodings and some of the common formats you will encounter with real-world data. We will learn how to identify character encoding problems and how to determine the proper type of character encoding for a particular dataset. We will write some...