![]() They already know what they'll find in the data before writing an actual query. The ones who do it well are intimately knowledgeable about what's in the data, what's missing, and everything in the world that that data touches. So how do journalists extract insights and powerful stories from even the most benign datasets. But this depth of data required the state legislature to care about the problem of racial profiling, and then to pass a law and allocate resources to properly collect the data. ![]() In contrast, every law agency in Connecticut publishes detailed data about every traffic stop, including the age, gender, race, and ethnicity of the driver, the reason the stop was initiated, whether the vehicle was searched, and what, if anything, was found. the age, race, and gender of the subject, while being vague about the reason for the stop and what happened during the stop: While Menlo Park publishes police stop data, it's almost entirely lacking information about who was stopped – e.g. Before the data is made publicly available, agencies can be overzealous in scrubbing it of the details that are not only interesting, but provide vital context needed to accurately analyze the data. That said, it's not easy to learn SQL with public data. We study public data because its free, its creation is a result of our tax dollars, and its contents and insights influence our laws and policies. Whoever first thought "If you didn't do anything wrong, what do you have to hide?" obviously didn't know SQL.
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