Ease Uncertainty by Boosting Your Data Science Team’s Skills

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Learning new skills

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:

  • Return to work in an office.
  • Send our children to school five days a week.
  • Go out to eat in a restaurant normally (whatever that means).

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:

  1. Tidymodeling with R: On September 17, 2020, RStudio’s Julia Silge announced this new book she is co-authoring with Max Kuhn. Julia and Max have put the first eleven chapters up online at www.tmwr.org and will be adding new chapters as new ones become available. This book is particularly important because it teaches how to use R and the tidymodels package while encouraging good methodology and statistical processes.
  2. 50 Do More With R videos: IDG’s Sharon Machlis has been creating short how-to videos for a couple of years now, and her collection has become quite comprehensive. The videos cover a wide variety of topics from spiffing up your ggplots to how to search, sort, and filter tweets by hashtag with rtweet and reactable. I think these videos could be particularly useful as the basis for a weekly “Lunch and Learn” program to build data science techniques.
  3. Knowledge Sharing is the New Gold: In this article, Shan Huang writes how her team built a Data Studio using RStudio Connect to share their insights better with others in her company. The system they’ve built achieves many of the goals of what we view as serious data science, including allowing their data scientists to work interoperably between R and Python and communicating better with stakeholders. To my mind, this piece does a great job describing the outcomes data science leaders should be aiming for with their teams; if nothing else, it provides a good set of talking points for your data science budget planning meeting.

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.

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