[Tutorial/Workflow] Data Science Workflows 101

[TinasheMTapera]

🧾 Summary

As an accompaniment to the data science workflows presentation, we should create introductory articles demonstrating each component of the 101 takeaway principles. These should be somewhat more comprehensive than the presentation, and follow a more narrative style that one can follow along and run code alongside where appropriate.

🎯 Learning Goals / Outcomes

What should a reader understand or be able to do after following this tutorial/workflow?

• Know about the original presentation slides
• Understand that the slides are teaching about the basic principles that guide data science in the lab
• Be introduced to each principle with well-thought-out and demonstrated tutorials

📦 Required Tools, Packages, or Data

See presentation

📄 Outline / Structure

Each principle should introduce one concept, discuss it at a high level, and then demonstrate how it works in practice. For this issue, only one example is necessary, but each principle could have any number of examples (e.g. Efficiency with FASRC will first cover RStudio Server jobs, but additional articles for VSCode, Remote desktop, loops and recursion, and slurm job submissions fall under this one principle).

1. Introduction
2. Setup
3. Key Steps
4. Conclusion / Next Steps

🔗 Related Materials

Link to related GitHub repos, issues, lab protocols, or previous examples:

• Presentation
• PennLINC for inspiration

🚦Status & Next Steps

What is the current status?

• Proposal
• In Progress
• Ready for Review
• Completed

Any notes on who will write or review the content?

Review should be requested by at least one student for each

[TinasheMTapera]

Review content thoroughly for accuracy and relevance

[TinasheMTapera]

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