Much has been written in statistics and data science education literature about pedagogical tools and approaches to provide a practical computational foundation for students. However a common friction point for getting students (and faculty) started with computing is installation and setup. If you’ve heard me talk about teaching R, you’ve probably heard me mention the following day one dilemma: Option 1 😰 Option 2 😎 1.

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Preparing to teach

I’m excited to announce that, with support from the National Science Foundation (pending final approval), the Section on Statistics and Data Science Education will host the 2nd annual Preparing for Careers in Teaching Statistics and Data Science Workshop in Fort Collins, Colorado, on July 27 (immediately prior to JSM 2019 in Denver, Colorado). The workshop is designed for graduate students and recent PhDs interested in careers in teaching statistics and data science.

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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.

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We’re discussing data visualization nowadays in my course, and today’s topic was supposed to be mapping. However late last night I realized I was going to run out of time and decided to table hands on mapping exercises till a bit later in the course (after we do some data manipulation as well, which I think will work better). That being said, talking about maps seemed timely, especially with Hurricane Irma developing.

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We’re discussing data visualization nowadays in my course, and today’s topic was supposed to be mapping. However late last night I realized I was going to run out of time and decided to table hands on mapping exercises till a bit later in the course (after we do some data manipulation as well, which I think will work better). That being said, talking about maps seemed timely, especially with Hurricane Irma developing.

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I’m a bit late in posting this, but travel delays post-JSM left me weary, so I’m just getting around to it. Better late than never? Wednesday at JSM featured an invited statistics education session on Modernizing the Undergraduate Statistics Curriculum. This session featured two types of speakers: those who are currently involved in undergraduate education and those who are on the receiving end of graduating majors. The speakers involved in undergraduate education presented on their recent efforts for modernizing the undergraduate statistics curriculum to provide the essential computational and problem solving skills expected from today’s modern statistician while also providing a firm grounding in theory and methods.

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Tuesday morning, bright an early at 8:30am, was our session titled “Novel Approaches to First Statistics / Data Science Course”. For some students the first course in statistics may be the only quantitative reasoning course they take in college. For others, it is the first of many in a statistics major curriculum. The content of this course depends on which audience the course is aimed at as well as its place in the curriculum.

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Citizen Statistician

Learning to swim in the data deluge