If you are immune to boredom, there is literally nothing you cannot accomplish.
David Foster Wallace
Data cleansing is the first step in any data science project, and often the most important. Many clever analyses have been undone because the data analyzed had fundamental quality problems or underlying artifacts that biased the analysis or led the data scientist to see things that weren’t really there.
Despite its importance, most textbooks and classes on data science either don’t cover data cleansing or only give it a passing mention. The explanation for this is simple: cleansing data is really boring. It is the tedious, dull work that you have to do before you can get to the really cool machine learning algorithm that you’ve been dying to apply to a new problem. Many new data scientists tend to rush past it to get their data into a minimally acceptable state, only to discover that the data has major quality issues after they apply their (potentially computationally intensive) algorithm and get a nonsense answer as output.
Everyone has heard the saying “garbage in, garbage out.” But there is something even more pernicious: getting reasonable-looking answers from a reasonable-looking data set that has major (but not obvious at first glance) quality issues. Drawing significant conclusions based on this kind of mistake is the sort of thing that gets data scientists fired.
One of the most important talents ...