But if anyone wants to become an expert into ML/DS, learning statistics and probability is fundamental. Books like A First Course in Probability, Introduction to Statistical Learning and Elements of Statistical Learning, to name a few, are very important.
A lot of the mistakes done in practice are based on a lack of understanding in sampling techniques, how statistical metrics can be misleading and so on.
First thing I learned in statistics is the difference between quantitative and qualitative information. If someone knows this before hopping into Kaggle, they know that categorical features can't be used as continuous features.
Though for applied papers that's sometimes enough.
But heed Larry Wasserman's advice: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid."
For the 101 level, I agree; I’d say you need a good understanding of basic probs and stats, rather than a vague understanding of advanced topics.
If you can spot when somebody says something about a dataset, the assertion is true only if the data is normally distributed, but there’s no checking about the actual distribution, you’re probably good to go.
This example: > It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model).
Doesn't seem related to stats or probability at all to me. Just critical thinking skills