Pre-Course thoughts

My motivation to take a MOOC was twofold.  First, I have never taken a formal course on statistics.  In my physics training, I have been taught enough stats to calculate error for experimental data and I know my way around a Gaussian, but I have never taken a stand alone course from a math background.  Second, as MOOCs gain popularity and more and more universities are jumping on the bandwagon, I wanted to experience one of these courses as a student.  I think my choice of classes will give me a pretty good perspective of what students' experience would be in these courses - I am not taking a class in my area of expertise and am coming to the subject with little background.

The first couse I signed up for was Statistics One.  From Coursera:
Statistics One is designed to be a friendly introduction to very simple, very basic, fundamental concepts in statistics. This course is, quite literally, for everyone. If you think you can't learn statistics, this course is for you. If you had a statistics course before but feel like you need a refresher, this course is for you. Statistics One also provides an introduction to the R programming language. All the examples and assignments will involve writing code in R and interpreting R output. R software is free! It is also an open source programming language. What this means is you can download R, take this course, and start programming in R after just a few lectures. Statistics may seem like a foreign language, and in many ways it is. The ultimate goal of Statistics One is to get people all over the world to speak this language. So consider this your first course in a new and exciting universal language!

The instructor was Professor Andrew Conwayis, a Senior Lecturer in the Department of Psychology at Princeton University.

I dropped this course quite quickly - I wanted a more mathematically intense course.  I watched the first few lectures before dropping.  Prof. Conwayis either lectured over slides or we saw him on camera in front of a screen with his slides on it.  He has some sort of tablet that allowed him to go forward on his slide presentation as well as write on the slides.  It looked like PowerPoint.  The reason I dropped was that I wanted a more mathematical statistics course - this course was more qualitative, and seemed to rely a lot on R to make what I felt was simple calculations.

Luckily another course started ~2 weeks later, Mathematical Biostatistics Boot Camp:

Statistics is a thriving discipline that provides the fundamental language of all empirical research. Biostatistics is simply the field of statistics applied in the biomedical sciences. 

This course puts forward key mathematical and statistical topics to help students understand biostatistics at a deeper level. After completing this course, students will have a basic level of understanding of the goals, assumptions, benefits and negatives of probability modeling in the medical sciences. This understanding will be invaluable when approaching new statistical topics and will provide students with a framework and foundation for future self learning. 

Topics include probability, random variables, distributions, expectations, variances, independence, conditional probabilities, likelihood and some basic inferences based on confidence intervals.

The instructor is Dr. Brian Caffo, associate professor in the Department of Biostatistics at the Johns Hopkins University Bloomberg School of Public Health. 

This course seemed to meet my needs better, and calculus was a pre-requisite so I was certain of its rigor.  I look forward to starting!