I took that course when it was running on Coursera, and I honestly can't recommend it (in its current state, at least) to anyone looking to learn basic statistics.
It covered a lot of material, but the quality and order of coverage was very inconsistent. The first couple weeks were fine, but it felt really odd to jump from correlations and scatterplots into regression, then come back to t-tests and AOV afterwards. There were also some errors in the R code on the slides, which led to a lot of confusion on the discussion forums during the class. As a student, it didn't feel like the class's pedagogical approach was very good, and I'm now finding myself using other resources to fill in the gaps.
If you'd like to hear more about those other resources I'd gladly post a list, but they're mostly Python-centric. One that I can whole-heartedly recommend even if you stick with Prof. Conway's class is the set of lectures from Roger Peng's "Computing for Data Analysis" class on Coursera. The course itself isn't available at the moment, but the videos are on his Youtube channel[1]. It teaches R from a programming perspective, and you'll find the content invaluable once you start writing R code that's more complex than a couple stats functions and a plot.
Hi, thanks a ton for the detailed response. Luckily I don't really need a Stats 101, so I don't think I'll mind him jumping around. If, of course, it does get a bother I know which course is right for me. Till then I'm also doing a bit of Thrun's Udacity Stats course on the side.
I would actually appreciate a list of resources in Python, that's what I like using most! I have downloaded a copy of "Think Stats", but haven't gone through it yet.
Sorry for the late response, I completely forgot about this post!
Looks like you're on the right track though, "Think Stats" and Udacity's stats class were the main things I was going to recommend. I'd also recommend checking out IPython's web notebook for inline charts and general awesomeness, and the Pandas library for an R-style data frame built on top of NumPy. The best resources for learning about IPython are probably screencasts, and the author of Pandas has a book out named "Python for Data Analysis" that covers IPython, NumPy, Pandas and some matplotlib.
It covered a lot of material, but the quality and order of coverage was very inconsistent. The first couple weeks were fine, but it felt really odd to jump from correlations and scatterplots into regression, then come back to t-tests and AOV afterwards. There were also some errors in the R code on the slides, which led to a lot of confusion on the discussion forums during the class. As a student, it didn't feel like the class's pedagogical approach was very good, and I'm now finding myself using other resources to fill in the gaps.
If you'd like to hear more about those other resources I'd gladly post a list, but they're mostly Python-centric. One that I can whole-heartedly recommend even if you stick with Prof. Conway's class is the set of lectures from Roger Peng's "Computing for Data Analysis" class on Coursera. The course itself isn't available at the moment, but the videos are on his Youtube channel[1]. It teaches R from a programming perspective, and you'll find the content invaluable once you start writing R code that's more complex than a couple stats functions and a plot.
[1]: https://www.youtube.com/user/rdpeng/videos?flow=grid&vie...