Hi, Glad you're finding it helpful! The reason I didn't include linear algebra is that it is possible to do the day-to-day work of most entry level data science jobs without it.
That said, it is great to know for a deeper understanding and if you are writing your own machine learning algorithms. This MIT OCW class provides a good introduction, with video lectures and problem sets: http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-...
Thanks for your comments! I completely agree about the lack of hands-on courses. I found the same thing when I was putting this together. The capstone project is our attempt at including something more practical, but it's self-directed, so that's not exactly what you are after. (Creating individual courses was outside the scope of this project.) However, I'm confident it will exist someday, given the current popularity of both data science and online courses. I assume you've also done some Kaggle challenges?
I agree with the suggestion that you should attend meetups and tech talks (or watch them online if there are none in your area). You'll hear more about real life examples and have a chance to ask questions.
The other main way to learn what you're asking is to get a job doing it! You have more than enough background (assuming you also have knowledge of tools) and you will learn more from others and as you need the information.
There is more discussion on this topic here: http://www.quora.com/Big-Data/What-concepts-of-linear-algebr...