Welcome to data science’s dirty little secret: data is messy. and it’s your problem.
It’s bad enough that data comes from myriad sources and in a dizzying variety of formats. Malformed files, missing values, inconsistent and arcane formats, and a host of other issues all conspire to keep you away from your intended purpose: getting meaningful insight out of your data. Before you can touch any algorithms, before you feed any regressions, you’re going to have to roll up your sleeves and whip that data into shape.
Q Ethan McCallum, technology consultant and author of Parallel R (O’Reilly), will explore common pitfalls of this data munging and share solutions from his personal playbook. Most of all, he’ll show you how to do this quickly and effectively, so you can get back to the real work of analyzing your data.
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