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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Data Science Webinar Announcement

I’m pleased to announce that on Monday, September 11 , 9-11 am Pacific, I’ll be leading a Concord Consortium Data Science Education Webinar. Oddly, I forgot to give it a title, but it would be something like “Towards a Learning Trajectory for K-12 Data Science”. This webinar, like all Concord webinars, is intended to be highly interactive. Participants should have their favorite statistical software at the ready. A detailed abstract as well as registration information is here.

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**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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Citizen Statistician

Learning to swim in the data deluge