Artwork by Allison Horst
As we all begin the fall planning and budgeting season, everyone I know is feeling a lot of uncertainty. I think that’s natural; after all, many of us still don’t know when we’ll be able to:
What hasn’t changed, though, is expectations for data science teams to deliver results. The global economic downturn due to the COVID-19 virus has hurt business revenues and profits for many organizations. That economic pressure suggests that data science leaders should think about how they can best demonstrate the value of their teams to the business, especially as we look forward to 2021.
In the past week, I’ve found a few new learning resources that I thought could help data science teams learn new skills and communicate their value better. They are:
tidymodelspackage while encouraging good methodology and statistical processes.
reactable. I think these videos could be particularly useful as the basis for a weekly “Lunch and Learn” program to build data science techniques.
We here at RStudio are also ramping up our efforts this fall to support more serious data science and interoperability in both our open source and commercial products. Check back with the blog regularly to catch all the announcements; we expect it to be a very busy fall.
And don’t forget to subscribe to receive updates on our very first rstudio::global(2021) virtual conference scheduled for early 2021. This will be our first completely virtual event, featuring 24 hours of speakers from around the world sharing their reflections on how they use R and extend it into new packages and communities. You’ll be hearing more about this exciting event in the coming weeks.
Many tools used routinely by software developers can also be useful to data scientists.
In this post, we explore possible challenges to putting Shiny in production and how to overcome them.