Stats and R...Sort of

Not sure how this section is going to look but trying to keep up with stats via Coursera, free classes taught by top university professors, some of which are quite challenging.  Okay, a number of the R programming courses are difficult, primarily because I am not a constant user.

Here's a Tip:

• If you are having trouble with your code, no matter the program you are using, examine the lines where the error is occurring.  Reading each part of the code slowly can help spot the error.  A friend of mine had code given to her by her professor to use in the homework program and yet when she ran the program, it stopped running at that line.  What was a week of frustration for her took only five minutes with fresh eyes to see that the professor had sent her class a typo in the code, putting IS where there should have been an IF.  Sometimes when you are too close to the project you cannot see the error.

Current Coursera Courses
Programming for Everyone (Python) (University of Michigan) ...this is a ten week course, that really explaining everything about programming format/syntax at an easy pace.

Getting and Cleaning Data (Johns Hopkins) ...good but tough course I need more practice with - do over, really

Regression Models (Johns Hopkins)

Previous Coursera Courses (and Quick Review):
9. R Programming (Johns Hopkins) Spring 2014
...If I could get the remote Git access to work, I could probably get the middle assignment done; still got a 92% for the course even without completing the second assignment.
8. The Data Scientist's Toolbox (Johns Hopkins) Spring 2014
A very light introduction to R and Github; still need more work with the latter.
7. Virology II: How Viruses Cause Disease (Columbia University) Spring 2014
The struggle of Virology I was worth it.  The only problem was not enough viruses and diseases!
6. Statistical Reasoning for Public Health (Johns Hopkins) Spring 2014
This was a good review course, though it felt like some of the quiz questions were recycled from another stats course.
5. Mathematical Biostatistics Boot Camp II (Johns Hopkins) Fall 2013
Great review for statistical concepts at the upper level course work without too much Calculus.
4. Computing for Data Analysis (Johns Hopkins) Fall 2013
That was an intense 4 weeks for a non-programmer but user of R. Learned a lot and find that I need more work with loops.
3. Virology I: How Viruses Work (Columbia University) Fall 2013
I really thought this was going to help me understand why viruses do what they do, but the class is much more biology based. I think I would have been happier with an epidemiology class.  Debating whether I want to attend Virology II.
2. Mathematical Biostatistics Boot Camp I (Johns Hopkins) Summer 2013
Very math/stats based; glad I had already finished my BS Stats degree by the time I started but good refresher.
1. Cased-Based Introduction to Biostatistics (Johns Hopkins) Summer 2013
(more of a front-end class looking at a couple of cases)

Thinking of working through the examples in Visualize This (and related blog Flowing Data), as well as Linear Models with R.

Update Apr 2014: Starting to learn Python...this should be interesting.

Update Feb 2014: Really glad I decided to continue with Virology II, as this is the class I had been look for with Virology I.

Update Oct 2013: Wish I had taken "Computing for Data Analysis" last year as this class, whilst short, is providing me with information about R I would/did not normally use, like loops - how they work and how to play around with them.  Have also just started "Mathematical Biostatistics Boot Camp II."