Professional Case Studies
summary
Over 200 customer conversations reveal concrete patterns: a Phase‑1 team moves from Excel to Shiny and uses RStudio Connect’s push-button deployment to publish apps, scheduled R Markdown and pins as ephemeral data stores for ETL, and email-triggered reports; a Phase‑2 team implements GitHub branch→staging→master workflows so Connect watches repos and performs an automated dev-test-prod workflow for safe redeploys. Server Pro, Connect and Package Manager provide the modular pieces that actually make these practices work.
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chapters (17)
- 0:00Intro
- 2:25Phases of Data Science Team Maturity
- 5:37Data Science in the Enterprise
- 6:49Phase 1 Team in a Nutshell
- 7:49Phase 1 Team: Success Strategy RStudio Connect provides a home for data science assets with push-button deployment from Rstudio or Jupyter IDES
- 8:44Phase 1 Team: Enabled Growth
- 9:21Report Scheduling
- 10:04ETL with R Markdown and pins
- 11:54Phase 2 Team in a Nutshell
- 13:50What is "Production" anyway?
- 14:43Incomplete Concept of "Production"
- 15:49Formalized Production
- 16:02Phase 2 Team: Success Strategy
- 17:49Demo: Automated Dev-Test-Prod with Git-backed Deployment
- 18:54There are no data science unicorns
- 19:21teamwork makes the dream work
- 19:34Wrapping Up
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