One of the nice things about developing 9 new courses for the JHU Data Science Specialization in a short period of time is that you get to learn all kinds of cool and interesting tools. One of the ways that we were able to push out so much content in just a few months was that we did most of the work ourselves, rather than outsourcing things like video production and editing.
Jeff and I talk about Jeff’s recently completed MOOC on Data Analysis.
I have been getting some questions via email, LinkedIn, and Twitter about the content of the Data Analysis class I will be teaching for Coursera. Data Analysis and Data Science mean different things to different people. So I made a video describing how Data Analysis fits into the landscape of other quantitative classes here: Here is the corresponding presentation. I also made a tentative list of topics we will cover, subject to change at the instructor’s whim.
Jeff and I talk with Brian Caffo about teaching MOOCs on Coursera.
A little while ago I wrote a post on statistics projects ideas for students. In honor of the first Simply Statistics Coursera offering, Computing for Data Analysis, here is a new list of student projects for folks excited about trying out those new R programming skills. Again we have rated each project with my best guess difficulty and effort required. Happy computing! Data Analysis Use city data to predict areas with the highest risk for parking tickets.
Not necessarily statistics related, but pretty appropriate now that the school year is starting. Here is a little introduction to “how to google” (via Andrew J.). Being able to “just google it” and find answers for oneself without having to resort to asking folks is maybe the #1 most useful skill as a statistician. A really nice presentation on interactive graphics with the googleVis package. I think one of the most interesting things about the presentation is that it was built with markdown/knitr/slidy (see slide 53).
Editor’s Note: This post written by Roger Peng and Jeff Leek. A couple of weeks ago, we announced that we would be teaching free courses in Computing for Data Analysis and Data Analysis on the Coursera platform. At the same time, a number of other universities also announced partnerships with Coursera leading to a large number of new offerings. That, coupled with a new round of funding for Coursera, led to press coverage in the New York Times, the Atlantic, and other media outlets.
Many academics are complaining about online education and warning us about how it can lead to a lower quality product. For example, the New York Times recently published this op-ed piece wondering if “online education [will] ever be education of the very best sort?”. Although pretty much every controlled experiment comparing online and in-class education finds that students learn just about the same under both approaches, I do agree that in-person lectures are more enjoyable to both faculty and students.
This paper is the paper describing how Uri Simonsohn identified academic misconduct using statistical analyses. This approach has received a huge amount of press in the scientific literature. The basic approach is that he calculates the standard deviations of mean/standard deviation estimates across groups being compared. Then he simulates from a Normal distribution and shows that under the Normal model, it is unlikely that the means/standard deviations are so similar.