Dirty data lurk everywhere: in text files, spreadsheets, databases, and PDFs. We'll walk you through some examples of the most common types of dirty data, point out telltale signs of data illness and explain how you can whip data into shape using some simple tools and methods.
This session will be most useful if: You have some experience working with data in columns and rows, in spreadsheets or database managers.
Sean Sposito, @seansposito, is a data reporter at the Atlanta Journal-Constitution.
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