I cannot tell you how frustrating this was for me. I wanted just the meat: the core mathematical concepts on which statistical models and inferences are built. Don't tell me a folksy story about gathering soil samples, show me the tools and what they can do, both their power and their limitations. I can think for myself about how to apply those concepts.
I loved this book for being exceptionally clear and terse. I was hooked from the first sentence: "Probability is a mathematical language for quantifying uncertainty." That one sentence makes the concept clear in a way that the entire chapter on probability from "Statistics in a Nutshell" (http://www.amazon.com/Statistics-Nutshell-Sarah-Boslaugh/dp/...) did not.
I'm not someone who thrives on theorems and proofs, I thrive on concepts. And I found this book dense with clear explanations of the key concepts.
O'Reilly's Statistics in a Nutshell is a good stats textbook and reference, has some good exercises to be done in R (or S or Matlab), and is generally more programming-oriented than most statistics treatments. http://oreilly.com/catalog/9780596510497/
Two important classics are Knuth's The Art of Computer Programming (more algorithms/programming-focused; very steep learning curve) and Concrete Mathematics (with Ron Graham and Oren Patashnik; more math-focused; may be easier for self-study). Both have exercises you can work through, though many of the exercises are proofs (not easily translatable to programming).
Get dozens of book recommendations delivered straight to your inbox every Thursday.