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For my third online course I opted for Statistics One - a 6-weeks introductory course on Statistics.

My choice was influenced by two factors: first of all some basic statistics have been part of all the course I took, and I am also planning to get another course focusing on Data Analysis, because it may help with some problems I need to analyse for job-related problems.

So in general I think that Statistics provide a good foundation and will probably play a key role in future studies.

The other reason was a bit more practical, even if in the end it's again a first step in the same general direction: this course had all the "homework" based on using "R".
So you get to learn the basics of it and play around a bit with data sets provided as part of the course material.

The course relied a lot more on longer videos - just the previous one (seems to be a characteristic of Coursera). I liked the teacher style in explaining things, even if a couple of times I was a bit overwhelmed.
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On the other hand, I persevere to invest the minimum amount of time possible to follow the lessons and send in the homework before the deadline. This is always complicated because I have plenty of things to do (including, at the same time, some other kind of studying, plus Aikido, Shodo and work…).

Final Result: 85% (by counting only the midterm and final exams). Usual caveats apply: course not very hard, so anyone with more energies/time to dedicate to this will surely fare better.
The "R" part was very interesting. I focused only the parts actually needed for the homework, but having a good experience as a developer I think I would have little problems in "grokking" more of it if needs arise, even if lots of people in the forums complained about "R" being hard to use or just plain weird.

Schedule:
Week 1
  • Lecture 1 ~ Randomized experiments vs. Observational studies
  • Lecture 2 ~ Descriptive statistics
  • Lecture 3 ~ Introduction to R
Week 2
  • Lecture 4 ~ Correlation
  • Lecture 5 ~ Measurement
  • Lecture 6 ~ Correlation analysis in R
Week 3
  • Lecture 7 ~ Regression
  • Lecture 8 ~ Multiple regression
  • Lecture 9 ~ Multiple regression analysis in R
Week 4
  • Lecture 10 ~ Mediation
  • Lecture 11 ~ Moderation
  • Lecture 12 ~ Mediation & moderation analysis in R
Week 5
  • Lecture 13 ~ Student’s t-test
  • Lecture 14 ~ Analysis of Variance (ANOVA)
  • Lecture 15 ~ ANOVA and t-tests in R
Week 6
  • Lecture 16 ~ Factorial ANOVA
  • Lecture 17 ~ Model comparison
  • Lecture 18 ~ Factorial ANOVA in R