Thread by @minebocek

This semester I had the pleasure (+ challenge) of teaching Regression Analysis. Much of the content was originally designed by the inimitable @MT_statistics, I’m handing it back w/ 3 updates:

🧹 #tidymodels
🔮 more predictive modeling
🔵 @quarto_pub!

🔗 sta210-s22.github.io/website

The curriculum now features a roughly equal dose of inferential and predictive models, using tidyverse and tidymodels for the #rstats code + all course content (website, slides, documents) created w/ Quarto. Source code for everything can be found at github.com/sta210-s22/web….

And importantly, students learned to use @quarto_pub as well! Roughly 75% (of 90 students) came in with one semester of R Markdown experience and the remaining with no R/R Markdown experience, and getting started with Quarto went without a hitch!

Interested in learning more about the curriculum & pedagogy for the course? Join @MT_statistics and me

🔗 at #eCOTS2022: causeweb.org/cause/ecots/ec…

🔗 and/or at #rstudioconf: rstudio.com/conference/202…

And finally, it’s so nice to be in a department with an open sharing culture for course materials, along with the source code for everything! A few examples 👇:

STA 199 - Intro Data Science
- by Alex Fisher: sta199-sp22-003.netlify.app
- by @jacobfhsmith: sta199-spring2022.netlify.app

Moar @StatSciDuke courses with open curricula:

STA 310 - GLMs by @MT_statistics: sta310-sp22.netlify.app

STA 323 - Stat Computing by @rundel:
- #rstats: sta323-sp22.github.io
- #python: sta663-sp22.github.io

STA 440 - Case Studies by Yue Jiang - www2.stat.duke.edu/courses/Spring…