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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.

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You could start looking at what offerings there are, for instance mooc's here and more traditional courses. Hopefully some other can answer based on actual experience.

Some general ideas: statistics 21 century, some discussion papers.

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    $\begingroup$ thanks for resurfacing this question. I've forgotten I've posted it.. I know to google of course, but everything seems the same, even if the authors claim it is for the 21st century. The only thing I found that is sort of what I'm looking for, is a 2007 paper by George Webb, which states that statistics should be taught differently: escholarship.org/uc/item/6hb3k0nz $\endgroup$ – nafrtiti Jul 23 at 12:07

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