Tag Archives: teaching

SWC-inspired 12 hours

I’ve recently completed 12 hours of teaching Introduction to Python and SQL for an audience of new Institute for Health Metrics and Evaluation (IHME) staff and fellows.  SWC is a gem! (I have been thinking this for a while.)

In retrospect, what worked and what might I do differently next time?

Some SWC mechanics that worked well: Live coding, Hands-on exercises, Sticky notes, Jupyter notebooks, and friendly teaching assistants.

Some things to change: Remember to give the big-picture framing for each section, Do more explanation of solutions after hands-on exercises, Share the syllabus ahead of time, and emphasize that this is *introduction* material.

Some changes that I made mid-stream: longer breaks (15 minutes every hour or so), connect the examples to IHME-specific domains.

I also did not use an etherpad until we got through Creating Functions (Section 6 in the Python Inflammation Lession). That might have been too much typing in the first two sessions, and it was definitely appreciated.

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Follow-up to the classroom-worthy visual analytics example

Did you have a chance to see the sum-to-fifteen game I learned about last week?

Here is an animated version of playing it:

And here is what it looks like if you arrange the numbers in just the right 2d grid:

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A classroom-worthy example of the power of visual analytics

I started reading an “economics of diversity” book recently, and stumbled across a great example of the power of visual analytics (included early in the book to demonstrate the value of diverse representations):

This game is hard, right? I mean I have to think about it to figure out a good move. But if you think of it visually, the right way, it is not hard.  I’ll leave it as a mystery for now, and say that I can imagine a classroom exercise on this when I next teach interactive data visualization again.


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Long tables, cold coffee, and late nights

Sherry Turkle: https://www.uni.edu/provost/sites/default/files/u29/how_to_teach_in_an_age_of_distraction_-_the_chronicle_of_higher_education.pdf

Reminds me of the math cafes of Poland pre-WWII that I read about. That sounded so fun to me when I was a student.

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Big Data Science resources

• The Oregon Health & Science University (OHSU) Department of Medical Informatics & Clinical Epidemiology (DMICE) and Library are pleased to announce the release of open educational resources (OERs) in the area of Biomedical Big Data Science. Funded by a grant from the National Institutes of Health (NIH) Big Data to Knowledge (BD2K) Program, OERs have been produced that can be downloaded, used, and repurposed for a variety of educational audiences by both learners and educators. Development of the OERs is an ongoing process, but they have reached the point where a critical mass of the content is being made available for use and to obtain feedback. The OERs are intended to be flexible and customizable and their use or repurpose is encouraged. They can be used as “out of the box” courses for students or as materials for educators to use in courses, training programs, and other learning activities. The goal is to create 32 module topics. Currently, 20 of the modules are available for download and use. For additional information, contact Bill Hersh at: hersh@ohsu.edu.

Also all on GitHub: https://github.com/OHSUBD2K/

I want to see this one: BDK32 Displaying Confidence and Uncertainty

it doesn’t exist yet, so I have to remember to check back when it does.

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ID Modeling Summer School

I’ve been spending the week at the Infectious Disease Modeling Summer School here at UW. It’s very interesting, and good for me to learn more about how people in my new field think (especially people in my new field, outside of my little institute…)

I’ve discovered a pet peeve during this week of presentations, though. I’ve seen a lot of numerical examples where the numbers work out perfectly… a little too perfectly. If you split 1000 people into an experimental and control group by choosing a random subset of 500, fine. But if you look within that group to see how many have a trait that occurs independently with probability 0.2, you do not often find exactly 100 in group A and 100 in group B. I think a little more complexity in the numbers makes the example easier to understand.

I’m sure that you, my loyal reader, can generate random numbers from a multitude of distributions, if you wanted to spend the time. But if you’re busy, busy, busy, then you can have wolfram alpha do all the work. It actually comes through for that one: “sample Binomial(500, .2)“.

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