Where to get started in data analysis I wish to break into the field of data science, more specifically, analysing data sets, extracting features and classifying them or identifying relationships.
However, I have no formal math background and am trying to re-learn it from the ground up.  I've started an introductory stats and probability course on Coursera, and am looking at some linear algebra via Khan academy but looking at the math I'm still quite lost.
I was wondering if anyone knew of a good introduction to data analysis text that was aimed at non-mathematicians, a "black box" approach, if you will.  That would allow me to learn some basic techniques while I progress in my math that I could be getting on with alongside my studies.
I've had a play around with MATLAB, sci-kit learn, scipy, numpy for Python but all the texts I use are very math heavy.
Is this unavoidable?  Should I just knuckle down with the math first? What would your advice be?
 A: Consider these Coursera courses:
https://www.coursera.org/course/compdata
https://www.coursera.org/course/dataanalysis
I don't have experience with them, but they look interesting.
Perhaps you could tell us more about your background so we can better recommend resources that fit your level.
A: Start with one of the contests on Kaggle. You don't have to win, but it will give you some data to work with and some room to experiment. @ACD is right that having a problem that needs solving involving data will give you context for the math.
As for the math itself, start with linear regression. Virtually regardless of the problem you're looking at, you can apply linear regression. It may be a wholly unsuitable approach, but you can do it and see the results and learn better ways as you go. You will have to keep referring back to the math as you are implementing the regression so you'll pick that up as you go along. As long as you know how to multiply matrices, you should be ok to start. There are other nuances like knowing what full-rank means, but that's one of those things you'll learn when you get an error message about aliasing. 
And as for learning materials, I would recommend Andrew Ng's machine learning course on iTunesU. The problem with textbooks is that you'll spend all your time reading and not enough time actually doing. Once you get your feet wet, then textbooks are a good reference for learning new methods and getting more depth on methods you have learned. 
A: Personally i found that Using R and RStudio is the best for data analysis.
It's extremely easy to pick up, and very efficient. There's many packages using R that allow for some pretty cool visualisations.
Here's a link to the basics of R:
https://youtu.be/_lnDe3t97vo
