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The upside of teaching decision trees first is that it allows me to spot the people that dropped out of the online course after week one right away ;)

Seriously, I see an amazing amount of people that deploy basic vanilla decision trees (no boosting/bagging, no particular thought about features etc) without any real thought or reason. It has become one of my main "ML smells".

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In m experience, 95% of DT optimization comes from pruning; not boosting/bagging.

How does someone use a DT without some feature engineering?

I mean - I like people who say they'd try a random forest first for a given problem because it's almost always a good answer.

But yes - feature engineering is almost always the difference then.

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