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3, 1, 2.

3: Get (back) into the swing of thinking about mathematics and algorithms.

1: Bayesian statistics is a principled, coherent, consistent, intuitive, complete framework for reasoning about uncertainty. A good foundation.

2. Traditional statistics is more random and ad-hoc, but can be more practical than Bayesian methods. (Bayesian models are well-motivated, but it can be impractical to compute exact answers and you'll have to switch to approximation techniques, some of which are simple/universal/slow, and others get fairly complex.)

I need to write a preface to answer this question, but the most important prereq is Python programming. The premise of the series is that if you can program (in any language) you can use that skill as leverage to learn about other topics.

So I would recommend

1. Think Python 2. Think Stats 3. Think Bayes

If you are strong in Python but weak in math, then I would also recommend #3, (Collective Intelligence, by Segaran)