**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.

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About a year ago I wrote this post:  I wasn’t teaching that semester, so couldn’t take my own advice then, but thankfully (or the opposite of thankfully) Trump’s tweets still make timely discussion. I had two goals for presenting this example on the first day of my data science course (to an audience of all first-year undergraduates, with little to no background in computing and statistics): Give a data analysis example with a familiar context

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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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My JSM 2017 itinerary

JSM 2017 is almost here. I just landed in Maryland, and I finally managed to finish combing through the entire program. What a packed schedule! I like writing an itinerary post each year, mainly so I can come back to it during and after the event. I obviously won’t make it to all sessions listed for each time slot below, but my decision for which one(s) to attend during any time period will likely depend on proximity to previous session, and potentially also proximity to childcare area.

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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