I teach statistics to under-grad engineer students who major in Information Technology (IT). My students first learn a (prerequisite) course on probability where they learn combinatorics, different probability distributions and the central limit theorem. Currently I teach a pretty traditional syllabus: point estimation, confidence intervals, hypothesis testing and simple linear regression (the syllabus is actually quite correlated with chapters 7-11 in Montgomery and Rumer's textbook).
I have added some examples in R to the lessons, but that is the only change I've performed so far.
I'm wondering if and how I can update the course. This is actually a follow up question on my previous question as to why does hypothesis testing focus on the mean. I'm wondering what are the most valuable practices that I can teach my students, and if there are different (and better, more dated) syllabuses out there which I am not aware of.