Book on data modeling I'm looking for reference to get a better understanding and overview of the methods for data modeling (mostly related to business questions like finding categories in data and setting of scoring functions for forecasting). After some internet research I couldn't single out a canonical reference. I have some very basic knowledge of statistics and I believe the topics that could be most useful for me would be SVM, Decision Trees, Clustering, Covariance Matrix, Correlations, Multivariate analysis ... just to name some keywords for the direction. Also I'd like to have some foundation to eventually dive into other statistical and data mining topics.
While the reference should introduce the concepts, I'd like it to be fast-paced (not chatty, not too many examples) and mathematically thorough while concentrating on applications. Of course there are specialized books on each topic, but I'd rather learn the basics of all of these topic quickly. So ideally one or two books for all of them together.
I cannot think of a different way to explain :)
Can you suggest good books? Or maybe just help me get some orientation in the vast field of statistics so that I know what to look for.
 A: Maybe Elements of Statistical Learning by Hastie, Tibshirani, and Friedman? You can find it on Amazon, or Tibshirani has a free PDF download on his website. It covers a fairly broad range of modern statistical modelling methods with decent rigor and good examples.
A: If you have some knowledge of R (or want to), this new book sounds like it might be useful to you.
R for Business Analytics, A Ohri.
It is a very quick and broad tutorial based overview of using R for many business analytics topics, with one chapter dedicated to data mining (using the R Rattle GUI). Don't expect a lot of formal math though.
*recent review
** It's often useful to learn a lot of the practical aspects of data mining and machine learning by learning alongside specific programming language examples. If you have a particular programming language you prefer, I could try to recommend alternatives.
***One other broad, but gentle, introduction to these areas (less practical examples, and not business based, but more mathematically inclined) is:
Introduction to Machine Learning, Ethem Alpaydin.

edit: In response to your comment. If Python is your language of choice, look no further.
Machine Learning, by Stephen Marsland is hands down the best practical book to pick up many of your requested topics, with many good code based illustrations to actually build and follow along.
On the Pandas side, you might have some interest in a current Coursera course (just 2 weeks in) that is Python oriented, with Python based applications towards Portfolio analysis, financial markets, and simulation backtesting (although it's a bit introductory, the instructor participates in a AI based Hedge Fund consulting firm).  Computational Investing Part 1, Tucker Balch
