Wrong but useful. Working @Twitter, tweeting about data science and people management.
"Wait, it's all her views?" 
"Always has been"
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The fact that the shape and volume of input data can change so quickly is what makes writing DS code hard. Statistical (and data) modeling is all about encoding assumptions, and the logs of a fast changing product can upend your assumptions in real time.
Rather than focus on changing the raw data, DSes are better suited to making their data transformations more robust to variation in said raw data. This can be hard to wrap your head around if you think data should always perfectly reflect what happened in your product.
But it's also why it's called data SCIENCE. It's about finding signals in noise. It uses similar tools to SWE, but it's a fundamentally different craft. Being crisp about this distinction saves you the grief of looking like an amateurish engineer
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