COP: Epic: Develop Entry-level Data Science Projects for Fall New Joiners

### Overview
We need to identify and develop 2-3 Data Science Projects focused largely on spreadsheet analysis, so newcomers from Calbright program will have meaningful work to start with as they grow in skills

## Updated Epic Action Items Draft

### Planning / Alignment

- [x] Review existing HfLA Data Science project template, One Sheet expectations, and Epic examples
- [ ] Clarify goals for the entry-level project pathway
- [x] Support new Calbright / beginner-level contributors
* [x] Provide meaningful spreadsheet-based data analysis work
* [x] Create a repeatable starter project structure that can be reused for future datasets
* [x] Help identify and mentor future peer reviewers, stage leads, and project leads
* [x] Draft initial proposal for starter project structure

* [x] Recommend starting with 2–3 spreadsheet-based projects
* [x] Recommend using the same full workflow across each dataset
* [x] Recommend adding SQL projects later after spreadsheet teams are stable
* [ ] Review starter project proposal with Andrew

* [ ] Confirm whether this approach fits the HfLA Data Science Community of Practice goals
* [ ] Confirm preferred number of starter projects for initial launch
* [ ] Confirm whether projects should be framed as labs, starter projects, or full HfLA projects
* [ ] Confirm expectations for project ownership, lead review, and contributor onboarding
* [ ] Confirm whether any datasets, civic topics, or partner priorities should be preferred or avoided

### Dataset Research and Selection

* [ ] Identify potential civic/public policy/DEI datasets suitable for beginner spreadsheet analysis

* [ ] Prefer LA City, Los Angeles County, Greater LA, or California datasets
* [ ] Prefer CSV, XLSX, Google Sheets, or easily exportable formats
* [ ] Prefer datasets with approximately 300–10,000 records after filtering
* [ ] Prefer datasets with enough categorical and numerical fields to support pivot tables, charts, and summary analysis
* [ ] Add candidate data sources to Resources section
* [ ] Evaluate candidate datasets for beginner suitability

* [ ] Topic relevance
* [ ] Spreadsheet performance
* [ ] Data cleanliness / complexity
* [ ] Ethical risk or sensitivity
* [ ] Storytelling potential
* [ ] Suitability for repeatable workflow
* [ ] Select 2–3 datasets for initial spreadsheet starter projects
* [ ] Document selected dataset sources, download dates, filters, and known limitations

### Starter Project Template Development

* [ ] Define standard workflow to be used across all starter spreadsheet projects

* [ ] Project overview and stakeholder question
* [ ] Dataset orientation
* [ ] Data dictionary / column documentation
* [ ] Data cleaning
* [ ] Exploratory data analysis
* [ ] Insight identification
* [ ] Visualization / presentation planning
* [ ] Final report or slide deck
* [ ] Final documentation and project closeout
* [ ] Create reusable project README / starter guide
* [ ] Create reusable contributor onboarding instructions
* [ ] Create reusable issue templates for beginner-friendly spreadsheet tasks
* [ ] Create peer review checklists for common task types

* [ ] Data dictionary review
* [ ] Data cleaning review
* [ ] Pivot table / summary table review
* [ ] Chart review
* [ ] Insight review
* [ ] Final deliverable review
* [ ] Create lead review checklist for final review before issue closure

### Project One Sheet Development

For each selected starter project:

* [ ] Draft official project name
* [ ] Write brief project summary
* [ ] Define stakeholder or intended audience
* [ ] Summarize project value add
* [ ] Document project history, if applicable
* [ ] Identify project partners, if applicable
* [ ] Define tools to be used for analysis and visualization
* [ ] Draft 6-month roadmap
* [ ] Add project resources and dataset links
* [ ] Review One Sheet draft with Andrew or Data Science leadership
* [ ] Revise and finalize One Sheet

### Issue Creation / Project Board Setup

For each selected starter project:

* [ ] Create project Epic or parent issue, if needed
* [ ] Create “Start Here” issue for new contributors
* [ ] Create dataset orientation issue
* [ ] Create data dictionary issue(s)
* [ ] Create data cleaning issue(s)
* [ ] Create exploratory data analysis issue(s)
* [ ] Create visualization issue(s)
* [ ] Create insight identification issue(s)
* [ ] Create presentation/report issue(s)
* [ ] Create final documentation issue(s)
* [ ] Add labels, milestones, and project board status fields as appropriate
* [ ] Confirm each issue has clear acceptance criteria
* [ ] Confirm each issue can move through HfLA review flow:

* Self-assigned
* In progress
* Peer review
* Lead review
* Closed

### Contributor Onboarding and Leadership Pipeline

* [ ] Define expectations for starter project contributors
* [ ] Define expectations for peer reviewers
* [ ] Define expectations for stage leads or emerging team leads
* [ ] Create guidance for how contributors can move from beginner tasks into peer review or stage lead roles
* [ ] Identify first possible peer reviewers / team leads during onboarding
* [ ] Document mentoring process for new reviewers and future team leads
* [ ] Create process for escalating blocked issues or unclear analysis questions

### Launch / Pilot

* [ ] Prepare starter project overview for first Data Science Community onboarding session
* [ ] Present available starter projects to new contributors
* [ ] Help contributors select or self-assign starter issues
* [ ] Monitor initial issue flow and project board activity
* [ ] Support peer review process
* [ ] Complete lead review for issues that pass peer review
* [ ] Track project bottlenecks, unclear instructions, or repeated beginner questions
* [ ] Revise templates/issues based on pilot feedback

### Evaluation and Next Steps

* [ ] Review how well the initial starter projects supported new contributors
* [ ] Identify which tasks worked well for beginners
* [ ] Identify which tasks need clearer instructions or smaller issue breakdowns
* [ ] Identify contributors ready to support peer review or stage leadership
* [ ] Decide whether to launch additional spreadsheet projects
* [ ] Decide when there is enough capacity to create a SQL starter project
* [ ] Document lessons learned for future starter project creation

### Resources/Instructions
- [MyLA311 Service Request Data 2024](https://data.lacity.org/City-Infrastructure-Service-Requests/MyLA311-Service-Request-Data-2024/b7dx-7gc3/about_data)

One Sheet Resources:
[One Sheet Template](https://docs.google.com/document/d/1BCNkA3cEox1qp2803r1uDAtffEszQ2P34ZDHo4dKM0Y/edit?usp=sharing)
[How to create a One Sheet guide](https://docs.google.com/document/d/14-nYNCjrMrC8lucUNBfLOFQZk2sWbkRlJgigWDAlqlI/edit#heading=h.z4tpbshl55b9)
Check issue #3 for sample one sheets

Data Dictionary Resources:
[USGS Explanation](https://www.usgs.gov/products/data-and-tools/data-management/data-dictionaries)