I've been heads-down this week preparing for some upcoming talks, so not as much blogging as usual this week. But there have been some interesting conversations on Twitter this week that you may be interested to check out if you're not on the platform.
Steph Lock shares her go-to R packages for every stage of the data science process:
My #rstats #datascience goto ?— Steph Locke (@SteffLocke) April 28, 2018
IO: odbc readxl httr
Sampling: rsample modelr
Feature Engineering: recipes
Modelling: glmnet h2o FFTrees
Evaluation: broom yardstick
Deployment: sqlrutils AzureML opencpu
Rachel Thomas and Jeremy Howard advocate thinking differently about AI development, and not falling into the trap of thinking “bigger is always better” when it comes to data (a sentiment I wholeheartedly agree with):
I wondered what was so different about Python compared to R when it comes to package management, and got some really thoughtful responses:
Serious question: I use R, not Python, and while there's the occasional version/package issue in #rstats it's rarely a big deal. But I hear about this from Python devs all the time. What's so different about Python that this is such a thing? https://t.co/g8ddQu2gpt— David Smith (@revodavid) April 30, 2018
Twitter definitely has its bad side, but there's a lot of really interesting conversation on the platform as well. Click on each tweet to see the conversations these generated.