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There are also no shortage of "Data Science" projects that are basically just showing off (e.g. somebody in a large org trying to get recognition for being advanced) or using an algorithm to justify a decision that somebody had already decided to make. In this case, it's okay to solve the problems with a few lines of code and a nice presentation.

But if you have an important data driven decision that is going to cost you a lot of resources (money, people, time, etc), you want the analyst to be able to speak to what the data and/or model is telling you and why. When you have this type of problem and it's "solved" by a few lines of SciKit Learn, you should be prepared to have a really bad day at some point in the future. This is the type of Data Scientist that draws the ire of people who do analysis with more depth.

Let's be blunt: firms needing real data science know where to go and find real data scientists. Anything less real, on both sides, is matter of opinion and here it comes free market.

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