(By the way, wish I had known about that paper before the talk -- we had a ton of overlap and would have loved to point people to it. Consider this a ringing endorsement!) https://t.co/cUdLVGdOWy
5/5
@Grady_Booch The best guidance I’ve found for my area is a preprint :)
https://t.co/zD5ClodeRH
Any book recs that help adapt traditional software development practices to data science and statistics would be much appreciated. Clean Code for data science…
Friday afternoon roll call! Share the #datascience paper that you keep going back to. For me it's @hspter 's Opinionated analysis development. So many great opinions that I share in my teaching and talks on reproducibility & workflows.
https://t.co/JBaG5HkG4L
@Letxuga007 @kara_woo This is a great opportunity (pandemic aside) for one of those blameless post-mortems as described in @hspter's "Opinionated Analysis Development"
paper: https://t.co/sCXr2q0QUG
slides: https://t.co/zJRtkEfozq
@richardedden @JohnsHopkins This is a good perspective for folks that do data analysis: https://t.co/Wowejhv7Di. It talks about the "blameless postmortem" as a key practice.
I am having really cool ideas this weekend thanks to simultaneously thinking about:
Runway PL and Fast Fashion by @jeanqasaur
https://t.co/bPnbnDCgwS
Speaking R by @AmeliaMN
https://t.co/GtmlR27S6w
Opinionated Analysis Development by @hspter
https://t.co/PmWBnAkxA9
@rgfitzjohn It was a nice article tho. Data versioning is a huge open question. @markvdloo et al have contributed extensively to data assurance, but formal data version control triggering re-rendering of analysis I have last heard in https://t.co/paPMI4P6ym
Is @wmlandau {drake} the answer?