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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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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Structuring Data in Middle School

Of the many provocative and exciting discussions at this year’s Statistics Research Teaching and Learning conference in Rotarua, NZ, one that has stuck in my mind is from Lucia Zapata-Cardona, from the Universidad de Antioquia in Columbia. Lucia discussed data from her classroom observations of a teacher at a middle school (ages 12-13) in a “Northwest Columbian city”. The class was exciting for many reasons, but the reason that I want to write about it here is because of the fact that the teacher had the students structure and store their own data.

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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.

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Citizen Statistician

Learning to swim in the data deluge