For each of the major worktypes in the life of a data science project, we've created an issue template to help guide you through the process and ensure you capture what you need.
There are issue templates for the following issue types:
Ask issues are for capturing, scoping, and refining the value-based problems your team is trying to solve. They serve as a live definition of work for your projects and will be the anchor for coordinating the rest of the work you do.
Data issues are for collaborating on gathering and creating the datasets needed to solve a problem. There are two primary types of data issues: Data Acquistion and Dataset Creation.
Data Acquisition issues are for gathering data from existing sources. The end goal is to load the data into your working environment via a data ingestion pipeline.
Dataset Creation issues are for collaborating on creating new datasets that are needed to solve your problem. This can involve combining existing datasets together in new ways or it can be creating data pipelines for ingesting new data from a new source.
Explore issues give us a way to provide quick summaries and TLDRs for the exploratory work we do. The goal of explore issues is to increase our understanding of the data and to share those insights with others.
Experiment issues are for tracking and collaborating on the various approaches taken to solve a problem and for capturing the results.
Model issues are for working to productionalize your successful experiments so that you can deploy them. This will often involve writing tests, creating pipelines, parametrizing runs, and adding additional monitoring and logging.