Story Card Request

Project: Card Title:
Card Document: (link here)

Milestones

(a card can belong to multiple categories)

[x] descriptive - simple data (re)-representation, doing summary statistics belongs to this category, we do this for all data sets
[ ] explanatory - testing hypotheses and / or comparing data points
[ ] predictive - any regression, model fitting, classification or clustering tasks
[ ] prescriptive - when you want to recommend any action to be taken (we do this rarely, if at all)

[x] Document metadata
[ ] Request platform resources like database instances by copying the request template https://docs.google.com/document/d/1SlnEmRneRIP5Aco1vK2KBIA2l2lH__m9OTp5a9ymtgk, filling it out and sending it to the infra team
[ ] Decide whether to load to database or S3 with proper metadata documentation
[ ] Use platform-request form to request resources for database and / or AWS account to write data files to S3
[ ] Review metadata and proposed data analysis

[ ] Clone repo from template
[ ] Set up access to GitHub repo for all team members
[ ] Set up a container from a suitable version of the Dockerfile template
[ ] Prototyping and testing analysis proposals
[ ] Review additional proposed data analysis identified through prototyping
[ ] Write code for reproducible data processing steps with proper version control & data lineage
[ ] Data science results produced and documented
[ ] Data science peer reviewed

[ ] Database deployed to cloud
[ ] Initial API repo created via cookiecutter, using templatized names
[ ] API developer confers with Data Visualization/Frontend teams regarding story card MVP
[ ] API developer confers with Data Scientists regarding all needed calculations, filters and queries, validation
perhaps using OpenAPI as a contract/organization first, can help understand the needs/requirements - https://swagger.io/docs/specification/about/
https://apievangelist.com/2018/04/03/openapi-is-the-contract-for-your-microservice/
[ ] Basic API in container
[ ] Tests are created to validate API and prevent regressions
[ ] API developer provides documentation on all endpoints, calculations, filters and queries
[ ] API developer creates metadata endpoints as further defined
[ ] Basic API deployed to cloud
[ ] API endpoint with all needed calculations, filters and queries is available
[ ] Final validation with Data Scientists regarding endpoint?

[ ] Concept clearly articulated through card title, visualization title/subtitle, card question(s)/action(s), and card context
[ ] Titles and context use consistent language (e.g., census tract v. neighborhood) and match grain of data used in the visualization
[ ] Visualization and component choices inline with data visualization best practices
[ ] All components needed available in Storybook
[ ] Components available in Storybook demonstrate all needed features
[ ] Follows data visualization and interface guidelines available in Storybook