For theoretical statistics, justifying why you'd use one method versus another, the graduate level classic is Cassela and Berger, Statistical Inference. This teaches you to design your own tests and compare Bayesian vs. frequentist methods and interpret the paradoxes - and is full of beautiful explanations - but it's not a bag of tricks.

Applied statistics book tend to be more specialized. There's plenty of cookbooks with statements like "and then you should do this regression and compare the p-value to 5%," but that's not what you want. Any of several books with titles like Applied Regression Analysis or Multivariate Statistical Analysis will be at a more substantial level. After that there are specialist topics - experiment design, survival analysis, heavy tailed distributions, robust statistics, multi-level modeling, asymptotics. Statisticians get involved in a lot of areas, from quality control in manufacturing to clinical trial design to economic forecasting to genetics, so there's a lot of overlap with other fields in the academic literature.

Econometric Analysis by Greene is the economists' favorite stats book, and is very self-contained, covering everything from computational tricks for Monte Carlo simulations top some very modern multivariate methods.

As a computer scientist/hacker, your natural starting point is Bishop. Pattern recognition and Machine Learning or perhaps http://www.ai.mit.edu/courses/6.867-f03/lectures.html

I've heard machine learning defined as the application of statistical methods to engineering; people with statistics degrees grumble that CS/machine learning types learn to do what's computationally feasible and not properly justify their methods, doing the sort of analysis you'd see in Casella and Berger.

In terms of software, a lot of statistical computing in the open source world now gets done on R platform. For modern computationally intensive methods like MCMC (Markov Chain Monte Carlo) the program BUGS/WinBUGS is a standard.

Also, http://videolectures.net/ has a whole lot of lectures (mostly from the Machine Learning perspective), starting with intro to probability and going right up to modern research.

http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Compute...

I have a math degree but didn't study any statistics. I used this book to teach myself. There may be more comprehensive books but this one is the best for learning the basics on your own.

Another classic is The Use and Abuse of Statistics by W.J. Reichmann. The treatment is not nearly as formal, but it is still a worthwhile read.

http://www.autonlab.org/tutorials/

http://www.zedshaw.com/rants/programmer_stats.html

Karatzas and Shreve "Brownian Motion and Stochastic Calculus" is also a popular book among quants.

I still haven't come across an equivalent book on statistics. If anyone knows a good book, I'd be very grateful if they'd post the details

http://www.stat.cmu.edu/~larry/all-of-statistics/index.html

Feller is a lot of fun, but a very strange suggestion for someone who apparently just wants to learn some statistics and get to work. Feller is for, like, reading by the fireplace with a glass of wine.

http://www.statsoft.com/textbook/stathome.html

is an excellent introduction to the subject

http://en.wikipedia.org/wiki/Athanasios_Papoulis

http://books.google.co.in/books?as_auth=Athanaszaios+Papouli...