A little over a year ago, we decided to propose a data visualization course at the first-year level. We had been thinking about this for awhile, but never had the time to teach it given the scheduling constraints we had. When one of the other departments on campus was shut down and the faculty merged in with other departments, we felt that the time was ripe to make this proposal.
Webinar Series: Data Science Undergraduate Education Join the National Academies of Sciences, Engineering, and Medicine for a webinar series on undergraduate data science education. Webinars will take place on Tuesdays from 3-4pm ET starting onSeptember 12 and ending on November 14. See below for the list of dates and themes for each webinar. This webinar series is part of an input-gathering initiative for a National Academies study on Envisioning the Data Science Discipline: The Undergraduate Perspective.
One of the many nice things about summer is the time and space it allows for blogging. And, after a very stimulating SRTL conference (Statistics Reasoning, Teaching and Learning) in Rotorua, New Zealand, there’s lots to blog about. Let’s begin with a provocative posting by fellow SRTL-er Tim Erickson at his excellent blog A Best Case Scenario. I’ve known Tim for quite awhile, and have enjoyed many interesting and challenging discussions.
This last weekend I helped Danny Kaplan and Kathryn Kozak (Coconino Community College) put on a StatPREP workshop. We were also joined by Amelia McNamara (Smith College) and Joe Roith (St. Catherine’s University). The idea behind StatPREP is to work directly with college-level instructors, through online and in community-based workshops, to develop the understanding and skills needed to work and teach with modern data. Danny Kaplan ponders at #StatPREP One of the most interesting aspects of these workshops were the tutorials and exercises that the participants worked on.
Part of the reason why we have been somewhat silent at Citizen Statistician is that it’s DataFest season, and that means a few weeks (months?) of all consuming organization followed by a weekend of super fun data immersion and exhaustion… Each year that I organize DataFest I tell myself “next year, I’ll do [blah] to make my life easier”. This year I finally did it! Read about how I’ve been streamlining the process of registrations, registration confirmations, and dissemination of information prior to the event on my post titled “Organizing DataFest the tidy way” on the R Views blog.
Last year I was awarded a Project TIER (Teaching Integrity in Empirical Research) fellowship, and last week my work on the fellowship wrapped up with a meeting with the project leads, other fellows from last year, as well as new fellows for the next year. In a nutshell Project TIER focuses on reproducibility. Here is a brief summary of the project’s focus from their website: For a number of years, we have been developing a protocol for comprehensively documenting all the steps of data management and analysis that go into an empirical research paper.
A few weeks ago I gave a two-hour Introduction to R workshop for the Master of Engineering Management students at Duke. The session was organized by the student-led Career Development and Alumni Relations committee within this program. The slides for the workshop can be found here and the source code is available on GitHub. Why might this be of interest to you? The materials can give you a sense of what’s feasible to teach in two hours to an audience that is not scared of programming but is new to R.
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