Casella & Berger's "Statistical Inference" is a nice introduction to basic probability theory and statistics. I found it pretty readable, and it's used for many 1st year graduate stat programs.

Duda & Hart's "Pattern Classification" is one of the best introductions to machine learning IMO. It assumes very little in the way prerequisites, which is nice for first time exposure.

Hastie & Tibshirani's "Elements of Statistical Learning" can be a little intimidating without having been exposed to the ideas of the previous two texts. Afterwards, however, it is a gem.

I would suggest "elements of statistical learning". If possible I would also try to study some econometrics which gives unparalleled insight into the correlation vs causation issue. You can think of econometrics as a branch of statistics that remained separate from the mainstream for historical reasons.

I also neglected statistics, it seems there's no avoiding it these days. What cured me was a MOOC from edx/MITx called 6.041x. It literally had me close to tears a couple of times. There was carnage, whining and general malaise. I couldn't imagine a better course for persistent programmers who don't know when to quit.

It's been offered during the spring term for the past two years, so maybe Feb 2016 will see the next run.

https://www.edx.org/course/introduction-probability-science-...

I haven't finished it yet, but what I've read of Wasserman's All of Statistics I've liked. The chapters are a bit terse, so I'd plan on doing a bunch of the exercises. The good news is that there are lots of exercises and most of them feel well chosen.