Summary
Congratulations! You have learned a lot of topics in this chapter. Data cleaning is a very important part of business intelligence analysis. In this chapter, you learned that cleaning data is a four-step process that can be remembered by SFCA (summarize-fix-convert-adapt).
Summarizing the data gives you a big-picture overview and provides a perspective on the data. This shapes your data cleaning strategies. Fixing flawed data can be tedious, but there are common practices to use. Converting data is important to get it in the right data type to support your analysis. Dates and times can be difficult, but tools help with this. Adapting your data to a standard is the key to setting a foundation for a successful data analysis. Standards may be given or you may design one.
Continuing to learn is also important as the packages and methods change frequently. Lastly, the topic of data cleaning is full of interesting ideas that you may find helpful. A recommended resource is An Introduction...
