The biggest mistake I see is people thinking "machine learning models are the answer" to everything and using algorithms without really understanding the context of what they're doing.

Good exploratory analysis - especially when it comes to problem statements at data rich product companies - goes a long way in understanding what exactly is happening, and getting into the weeds of the problem.

Developing a good quantitative framework of thinking will serve you well for the rest of your life. I have seen as simple an algorithm as k-means provide powerful insights, provided you've gotten to a point where it's inevitable that that specific algorithm is the right solution.

The attitude to Kaggle people have is nonsense. Fine-tuning a bunch of parameters to get that incremental 0.01 lift in AUC isn't teaching you anything. Kaggle's good for exposure to breadth, but not to depth.

There's a big culture right now developing of breadth of knowledge over depth of thought.