I think interpretation is the hardest part. And I think it's the part we've got least far with developing. But it is super important. It's almost like part of the Hippocratic oath for data scientists ought to be, we're not going to say it's your fault if you interpret my beautiful output wrongly. So there's lots that we can do by hand. We can label things clearly. We can write out sentences. We can highlight things. We can have. You'll see on a lot of graphs these days, people actually write, more is better.
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Nick Radcliffe, data scientist and entrepreneur, talks to us about the importance of test your data. As software engineers we are familiar with test driven development. Test driven data analysis puts the same emphasis on validating and testing data for your AI app. We also dive into the Python library of the same name tdda.
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