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Example story card 1 #11

@karenng-civicsoftware

Description

@karenng-civicsoftware

Story Card Request

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

Milestones

Setup

Type of data processing / analysis this story card uses

(a card can belong to multiple categories)

  • 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)

Data documentation and proposed analysis

  • 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

Set up data processing development environment

  • 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

Build APIs

  • 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?

Data visualization:

  • 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

Design

  • TBD Wireframes?
  • TBD Design review?

Written content / additional links

  • Write content
  • Review content

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