Having addressed missing and duplicate entries, we now focus on ensuring data is stored in the correct format, known as its data type. Data analysis tools and algorithms rely heavily on data types. Attempting mathematical operations like addition on data stored as text, or sorting dates treated as simple strings, can cause errors or yield unreliable results. For instance, an operation like $5 + 10$ behaves differently if '5' is text instead of a number.

This chapter introduces common data types found in datasets, such as numeric (e.g., integers like $100$, floats like $98.6$), strings (text), booleans ($True$/$False$), and datetime formats. You will learn how to inspect the current types assigned to your data columns and, importantly, how to convert them to the appropriate format. We will cover techniques for converting data to numeric, datetime, and string or categorical types, including ways to handle issues that may arise during conversion. Properly setting data types is a necessary step for accurate analysis and preparation for subsequent tasks.