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I'd like to echo the comments about not wanting to be p-value police. I feel like in some of my courses that don't have a Stats prefix, I get to show students the great power of analysis, prediction, optimization, etc., but then in my Stats class, I find myself spending more time on what we can't do: incorrect interpretations of p-values, CIs, causation, etc.  It doesn't feel empowering, and I'd like to get away from it.

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At Macalester College, we have a data-science minor. The introductory course attempts to orient students to what data science is. The course uses a book, "Data Computing" (see http://data-computing.org).

Things to note:
* the book uses a very compact set of R commands, in the spirt of Randy Pruim's "Less volume, more creativity" approach from his Tuesday eCOTS session.
* the book is intended for a short course, about half a full course. For a full course, this can be supplemented with either more projects (data cleaning? data scraping?) or topics such as statistical modeling or machine learning.

This note isn't entirely a shameless plug. I have some copies that I'm happy to loan out to people, but first check the web site to see if you're really interested. Write to kaplan@macalester.edu to request a copy.

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shameless plug for Deb's Data Science case studies book

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shameless plug for Deb's Data Science case studies book

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https://www.crcpress.com/Data-Science-in-R-A-Case-Studies-Approach-to-Computational-Reasoning-and/Nolan-Lang/p/book/9781482234817

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Computer science is more than just programming; it is the creation of appropriate abstractions to express computational structures and the development of algorithms that operate on those abstractions.  Similarly, statistics is more than just collections of estimators and tests; it is the interplay of general notions of sampling, models, distributions and decision-making.  Data science is based on the idea that these styles of thinking support each other.

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Ideas for good data-science problems:
http://www.kdnuggets.com/2015/07/guide-data-science-good.html
The Definitive Guide to doing Data Science for Social Good

and
https://dssg.uchicago.edu/
though it would take a bit of digging to get a problem we can give to students.

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The changing educational climate has emboldened me to teach what actually takes most of my time and psychic energy as a data analyst: data cleaning, visualization, and wrangling. Ten years ago, I was much more sheepish about this—I worried it wasn’t `statistical’ enough. Now I have no shame.” (Jenny Bryan, UBC, Amstat Newshttp://magazine.amstat.org/blog/2016/03/01/introstats16/)

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Our students know how to deal with n → ∞, but cannot deal with a million observations.

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Data Science specialization at Johns Hopkins through Coursera: this is a great opportunity for faculty development

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The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

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The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

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A great preprint (Nolan and Perrett) on data viz as a key capacity can be found at

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There are lots of useful resources and papers in the TAS 2015 special issue (access is free for ASA members, editorial on "thinking with data" open access http://amstat.tandfonline.com/doi/full/10.1080/00031305.2015.1094283 )