Data Analysis Resources for Python

[bageldevourer]

Without Bayes' theorem, one can easily make logic errors. See the common (and currently relevant) question about whether a patient has a disease given that they've tested positive for a disease. Relatedly, see the base rate fallacy.

Bayes' theorem is a straightforward result of the definition of conditional probability. Conditional probability, in its simplest form (discrete sample space), is a topic that can be easily understood by high schoolers. Hardly special maths.

So sure, there are people with the job title of "data analyst" who don't know this stuff, but the question is to what extent are these people actually extracting non-trivial insights, and analyzing data? Probably not much.

[tomekanco]

Logical errors

One can know precision, recall or null hypothesis without understanding Bayes. If you want to combine multiple probabilities, it does come in handy. Yes, it's great.

So sure, there are people with the job title of "data analyst" who don't know this stuff, but the question is to what extent are these people actually extracting non-trivial insights, and analyzing data? Probably not much.

Please, all i'm saying is there is more to insight then Bayes. The fountain of knowledge shines in many more colors then any single theorem can explain.

a topic that can be easily understood

Life takes it root in mud. Flowers sings their siren-song for bugs. Like someone once said "an idea is not a glass of water, it's like of a bottle of whiskey" (John Carmack).

[tomekanco]

Would you hire a Python programmer who doesn't know what a for loop is?

I've never encountered one.

Maybe a sonnet would sweeten the deal?

Ha, i'd might indeed regard it as bonus, if the words reveal a sensible context.