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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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Jake’s exposition of Bayesianism

Still a gem:

  1. http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/
  2. http://jakevdp.github.io/blog/2014/06/06/frequentism-and-bayesianism-2-when-results-differ/
  3. http://jakevdp.github.io/blog/2014/06/12/frequentism-and-bayesianism-3-confidence-credibility/
  4. http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
  5. http://jakevdp.github.io/blog/2015/08/07/frequentism-and-bayesianism-5-model-selection/

(A collection of gems?)

And, TIL, (editors note, actually a few months ago…) there there is a paper version of it, too: https://arxiv.org/pdf/1411.5018.pdf

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Not the numbers

I’m working on conflict epi stuff again, and it is a challenge.  Here is a short film that I ran into which tells some of these stories a different way than our numbers.

https://www.shortoftheweek.com/2018/11/06/laymun/

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Machine learning in population health: Opportunities and threats

My colleague Theo Vos and I have a perspective published recently in PLoS Medicine, Machine learning in population health: Opportunities and threats. It is not long, so you can skim it in seconds, or read it all in just minutes.

It is not directly related to a short film that I enjoyed recently.  Maybe indirectly.

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Permutation Game

This summer my kids got a gift of a somewhat boring board game, where players were charged with tracing through a jumble of lines to find out what creature got to eat what dessert.  The name escapes me now, and I escaped taking the game home, too.

But it was not all bad—the mechanics of the game made me google “permutation game” which came up with a fun puzzle (and annoying interview question; this phrasing is from a website that helps people cram for job interviews):

Alice and Bob play the following game:

They choose a permutation of the first  numbers to begin with.

They play alternately and Alice plays first.

In a turn, they can remove any one remaining number from the permutation.

The game ends when the remaining numbers form an increasing sequence. The person who played the last turn (after which the sequence becomes increasing) wins the game.

Assuming both play optimally, who wins the game?

This game turns out to be kind of fun, at least if you find this sort of thing fun. I also tried a twist where we started with a string of letters, and crossed them off until they make a word.

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Bootstrap for uncertainty in CSMF estimation

[notebook]

download.png

 

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One more IDV in Python approach

https://plot.ly/dash/
https://community.plot.ly/c/dash
https://github.com/plotly/dash
https://plot.ly/dash/getting-started
View story at Medium.com

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