CAR wash: Techniques for cleaning dirty data
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.
Nate Carlisle covers polygamy and a view other things at The Salt Lake Tribune. He also teaches investigative journalism at the University of Utah. He has a bachelor's degree in journalism from the University of Missouri. Twitter: @natecarlisle
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