| This might not really translate to you since the context I have taken online courses in is that I have wanted to learn more so that I could be a more effective software engineer. So I'm looking for things that show applications quickly. Courses I've gotten a lot out of: Andrew Ng's ML course and Yaser Abu-Mostafa's learning from data course. In both courses I really felt like I was learning something meaningful. I very clearly remember how mind blowing it was to me that you could turn complex high level tasks into curve fitting. I excitedly told a colleague of this thing I had learned about called linear regression who turned out to be surprised I had not taken a statistics class. Same thing for generalization bounds & VC dimension in Yaser's class. I don't know if this can really be generalized to all courses since it might just be matching students to courses that make sense to them, but the worst line in a MOOC is "Well, this is boring, but you're just going to have to grind through it so that we can solve problems later". Shout out to the Convex Optimization MOOC I dropped. For Data Science 101 this should be easy. Find some problems that students could imagine wanting to work on and use those problems to drive what you talk about. Also, a pet peeve of mine is courses trying to make me do math by hand. I am 100% going to ask a math package to do an integral for me every time this happens. Sure, somebody has to know all the math deeply, but it's not me. So I guess the main takeaway here is that you need to understand your audience. What is appropriate for me is probably not appropriate for 1st semester college kids. |