The list below is a (non-comprehensive) selection of what I believe should be taught first, in data science classes, based on 30 years of business experience. This is a follow up to my article Why logistic regression should be taught last.
I am not sure whether these topics below are even discussed in data camps or college classes. One of the issue is the way teachers are recruited. The recruitment process favors individuals famous for their academic achievements, or for their "star" status, and they tend to teach the same thing over and over, for decades. Successful professionals have little interest in becoming a teacher (as the saying goes: if you can't do it, you write about it, if you can't write about it, you teach it.)
It does not have to be that way. Plenty of qualified professionals, even though not being a star, would be perfect teachers and are not necessarily motivated by money. They come with tremendous experience gained in the trenches, and could be fantastic teachers, helping students deal with real data. And they do not need to be a data scientist, many engineers are entirely capable (and qualified) to provide great data science training.
This article has three parts:
- Topics that should be taught very early on in a data science curriculum
- Topics taught in a traditional curriculum
- Topics that should also be included in a data science curriculum
Read the full article here.
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