I’m giving a two hour intro to statistics in a month as part of a data science bootcamp for incoming UCSB grad students. I want to use my time to introduce the students to Bayesian inference and Stan so they can be inspired to probabilistically model their data.

I know two hours isn’t much, so I was wondering if people had thoughts on what would be the best topics to cover and in what order. Ideally I’d like topics that would stick with the students and motivate them to learn more on their own. The students are incoming computer science, engineering, and biology grad students. I doubt these students will have much experience with statistics other than maybe basic intro to stats. They also might know some optimization, linear algebra, and basic mainstream machine learning.

To those who have given tutorials to that kind of audience, which topics did they find most interesting? Also what have you found is the most efficient order to cover topics? If anyone has a rough outline laying around that’d be really helpful.