CoP: Data Science: Create Data Ops Tutorial

### Overview
We need to have a Data Ops Tutorial page that will answer any q

### Action Items
- [ ] Create a Google Doc in the folder provided under resources
- [x] Draft an introductory paragraph explaining what the tutorial resources cover and why a new data scientist would use them for working with data at Hack For LA
- [x] Identify resources with vetted tutorials covering important skills within the tutorial area, adding to the draft
- [ ] Write Clear Learning Objectives
- State what the learner will *be able to do* by the end of the tutorial (e.g., “Set up a basic DataOps workflow with GitHub and Google Sheets”).
- [ ] Write Prerequisites
- Required skills (e.g., basic Python, Git/GitHub familiarity).
- Required tools/accounts (e.g., GitHub, Google Drive access, Looker viewer permissions).
- [ ] Write Setup Instructions
- Links to repos, sample datasets, or starter scripts.
- Environment setup (e.g., clone repo, install dependencies).
- [ ] Write Step-by-Step Walkthrough
- Guided example showing one DataOps pipeline end-to-end.
- Screenshots or code snippets illustrating key steps.
- [ ] Write Hands-On Exercise
- A small project/task learners can replicate (e.g., build a lightweight validation script, update a dashboard, or run a data ingestion pipeline).
- [ ] Write Common Pitfalls & Troubleshooting
- Document frequent mistakes (e.g., schema mismatch, GitHub permissions).
- Tips for debugging errors in automation or dashboards.
- [ ] Write Discussion / Reflection
- Prompts for learners to connect the tutorial to civic tech use cases.
- Questions like: “How would you adapt this pipeline for a different dataset?”
- [ ] Write Next Steps / Advanced Topics
- Suggested follow-ups (e.g., CI/CD for ETL pipelines, adding alerting, scaling beyond Google Sheets).
- [ ] Write Glossary / Key Concepts
- Definitions of terms like *ETL*, *schema governance*, *human-in-the-loop*.
- [ ] Add additional content to References & Resources
- link to relevant Hack for LA repos or dashboards (where possible).
- [ ] Review the draft with the Data Science CoP
- [ ] Add to the wiki page

### Resources/Instructions
#### Wiki page
[Data Ops Tutorial](https://github.com/hackforla/data-science/wiki/Data-Ops)

#### Location for any files you might need to upload (drafts, images, etc.)
- [Folder for files related to the Data Ops tutorial](https://drive.google.com/drive/folders/1j5r7SDEHAUhUbI6DDD3MxvobPfU9WiH-)
- [DS: Data Ops Tutorial Google Doc](https://docs.google.com/document/d/15WdOrTtKGuZL3rPuQm3h4SEWuUL4vML0XtPas8fBIwc/edit)

#### Tools that are core that should be mentioned:
- EC2
- Lambda
- RDS
- Athena/Hive
- Flask

#### Examples of resources that would be useful to include:
- Web how-to/tutorial/walk-throughs
- Youtube playlists or videos demonstrating tools
- Links to blogs or platforms with subject matter experts