The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis. I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data, courtesy of Redditor fevaah , showing old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't). If this idea was help...
MOOCs aren't just for current students. I think they can serve as professional development for faculty members as well. I don't have time for a MOOC during the school year, but I am committing to doing one this summer.
I think that instructors can approach MOOCs in two ways: 1) professional development, and 2) a search for improved pedagogy.
As professional development, learn a new statistical skill or freshen up a dormant one. Learn R. Learn Python. Freshen up on your non-parametric skillz. Take a course on data mining or using statistics in order to gain business insights.
Unofficial documentation of your course progress is typically offered just by taking the course. Official documentation/grade reports are usually available for a reasonable fee (my husband has taken a few such philosophy courses and paid around $50 for the official documentation).
Another way to use these courses: Don't take them to learn new skills, take them to learn new ways to teach your old content. Steal a good discussion board prompt. Find a new text book. Discover a useful interactive website that explains effect size or confidence intervals.
Some universities advertise their own MOOC lists, and I welcome you to Google around and find different classes. Here are the data science/statistics courses for two of the big MOOC organizations, Coursera and EdX.
I think that instructors can approach MOOCs in two ways: 1) professional development, and 2) a search for improved pedagogy.
As professional development, learn a new statistical skill or freshen up a dormant one. Learn R. Learn Python. Freshen up on your non-parametric skillz. Take a course on data mining or using statistics in order to gain business insights.
Unofficial documentation of your course progress is typically offered just by taking the course. Official documentation/grade reports are usually available for a reasonable fee (my husband has taken a few such philosophy courses and paid around $50 for the official documentation).
Another way to use these courses: Don't take them to learn new skills, take them to learn new ways to teach your old content. Steal a good discussion board prompt. Find a new text book. Discover a useful interactive website that explains effect size or confidence intervals.
Some universities advertise their own MOOC lists, and I welcome you to Google around and find different classes. Here are the data science/statistics courses for two of the big MOOC organizations, Coursera and EdX.