Developing a self-study plan I see that some previous questions have danced around this topic, but I can not find any answers that truly hit on my scenario.  
High Level Goal: Develop a sequential self-study plan aimed at developing skills necessary for weekend-warrior statistical analysis (a la 538-blog).  
Details:  Looking to generate a sequence of topics, with references to books and other online materials (EdX, MIT OCW, Khan etc.).  Off the top of my head I would like the topics to range from intro prob/stat to more complex statistical methods.  I am not afraid of mathematical rigor and would prefer it if we stayed away from layman references.
About Me:  Degree in neuroscience, took calc, lin alg, discrete math, and intro stats for neuroscience.  No work in pure probability or other higher level mathematics, so I would love references to necessary skills here if they apply.  I can program at an introductory level in Python and Java. Also, I have experience with SQL and some SAS (more mechanical use, not 'knowledge')   In the past I have attempted self-study, but end up not getting creating a logical flow and having large amounts of overlap.  
basic subjects interested in
1) prob/stats
2)regression analysis
3)time series
4)econometrics
5)modeling
I know this is a huge ask, and I am certainly not expecting any one person to hand it all to me, but I would really appreciate input from people with knowledge of what skills are necessary and the best resources to acquire them!  I'll certainly keep track of suggestions and then make my completed plan available.  Thanks in advance.
 A: The specialist track in Coursera from John Hopkins for Data Science seems to be perfect for your needs (if I am interpreting your needs correctly).  
https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage
The classes are designed for R, but given your background and their intro to R classes (at the beginning of the specialist track) you shouldn't be slowed down at all by the programming language.  All of the classes have additional links or texts suggested for further study if you want a deeper understanding of theory.  
Oh, and it's free! (unless you want to earn their specialist degree.  In that case, it costs money.)
Good luck!
edit: Depending on your exact goals and given that you haven't had a probability course, I would recommend getting an probability book to complement the course for background.  Specifically, I used Hogg and Tanis in my college class and actually think it's pretty great (I still reference it every now and then).
http://www.amazon.com/Probability-Statistical-Inference-Robert-Hogg/dp/0321584759/ref=sr_1_1?ie=UTF8&qid=1415908500&sr=8-1&keywords=Hogg+%26+Tanis+-+Probability+%26+Statistical+Inference
Again, depending on your exact goals, I would order the book and set up a schedule for getting through all of chapters 1-4 (making sure to grasp concepts, possibly working so many problems from each chapter).  Then I would make sure to hit 6.1, 6.2, 8.1 possibly 8.3, and most of chapter 9 (these aren't all the areas that would be touched on in the Coursera course, but I would reference the others during the course, not prior to it).  From there, I would start the Coursera course work and reference Hogg and Tanis whenever necessary.  The book is focused on probability and statistical inference (not regression), but the Coursera course is pretty focused on the practical (how we do it) parts of data science and this book, I believe, would help someone with a math background but little probability experience understand the more conceptual (why we do it and how it works) type of questions.
There are loads of good intro books out there though, if that's a direction you are strongly considering.
