- The course focused on using the R statistical software package, something I've had a passing familiarity with, but not a working knowledge of. As a result, I was in a good position to take this course. In addition, I have a few projects that require large amounts of statistical analysis, so I was highly motivated to learn the material.
- I really liked that videos that covered new concepts were relatively short (< 7 min.) while videos demonstrating how to use technology were longer (< 15 min.) This chunking of the content, with emphasis on modeling behaviors, helped me to pace myself and quickly demonstrated the commands I was to use.
- The programming assignments provided natural transference of the materials covered in the video lectures, to actually using them, and my understanding of them. The course also provided a numbers of ways, means, and resources to problem solve on your own. Having done a little bit of programming in other languages, I understood that finding solutions on your own is an important part of creating code. On the other hand, some students did complain that there wasn't enough 'instruction'.
- The programming assignments were decent, but I wish there was a way for students to compare full solutions. From discussion forums, it seemed that students used one set of functions (apply and its derivatives), while I didn't.
- I did find a few students in discussion forums that I regularly discussed assignments with, through the course website. I did not participate with the Meetups, as not having enough time to participate in them. If it were a longer course, with more in-depth assignments, I would probably do so.
I'm currently 'taking' Computation Investing, Part I, but am not participating in the assignments. I'm signed up for a few other Coursera courses, but they don't start until next year. They are; Data Analysis, Fundamentals of Online Education: Planning and Application, Passion Driven Statistics, The Science of Gastronomy, and Machine Learning.