We have covered a few key topics in this chapter to help you to improve your data literacy by learning about working with databases and using SQL. We learned about the history of SQL and the people who created the foundation for storing structured data in databases. We walked through some examples and how to insert records from a SQL SELECT statement into a pandas DataFrame for analysis.
By using the pandas library, we learned about how to sort, limit, and restrict data along with fundamental statistical functions such as counting, summing, and average. We covered how to identify and work with NaN (that is, nulls) in datasets along with the importance of data lineage during analysis.
In our next chapter, we will explore time series data and learn how to visualize your data using additional Python libraries to help to improve your data literacy skills.
