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@@ -77,6 +77,14 @@ libraries from scratch.
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go through during the exploration, synthesis, modeling and narration
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phases.
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*[Forget privacy: you're terrible at targeting anyway](https://apenwarr.ca/log/20190201)
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is a different type of article. It is a strong piece of commentary rather
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than a tutorial on a specific data analysis topic. The author argues that
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*collecting* data is typically easy but doing the dirty analysis work often
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yields little in the way of definitive, actionable insight. Overall it's
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a well-written thought piece that will make you at least stop and ask
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yourself, "do we *really* need to collect this user data?"
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*[Gender Distribution in North Korean Posters with Convolutional Neural Networks](http://digitalnk.com/blog/2017/09/30/gender-distribution-in-north-korean-posters/)
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is a fascinating post that uses convolutional neural networks as a
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mechanism to identify gender by faces in North Korean posters. The
[The Full Stack Python Guide to Deployments](http://www.deploypython.com/),
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[The Full Stack Python Guide to Deployments](https://www.deploypython.com/),
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[Python for Entrepreneurs](https://training.talkpython.fm/courses/explore_entrepreneurs/python-for-entrepreneurs-build-and-launch-your-online-business),
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[Coding Across America](http://www.codingacrossamerica.com/),
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