Open Source Checklist
This serves as a rough guideline for software projects that we will release as open-source. We don’t need to fulfill every single requirement for the Initial Release, but we should constantly update the fields as our project grows.
- Did we provide a README.md with basic information and setup on the project?
- Did we provide an API documentation for the project?
- Do we have a contributing guide in CONTRIBUTING.md?
- Do we have a CHANGELOG.md to document our releases?
- Do we have an OSI-approved LICENSE?
- Does our code follow the community style guide for our language of choice?
- Did we set-up automated testing for our business-logic code?
- Did we set-up continuous-integration/continuous-deployment (CI/CD) for the project?
- Do we follow semantic versioning guidelines?
- (Optional) Do we enforce style checks in our codebase?
- (Optional) Did we make our project accessible via package managers? (pip, npm, etc.)
- Do we have a Code of Conduct linked to our README?
- Do we have a project roadmap?
- (Optional) Did we provide Issue and Pull Request templates?
- (Optional) Did we set-up a chat client (e.g. Gitter) for our project?
We tackle some of the nitty-gritty, advanced statistical concepts that must be addressed by statisticians and data scientists when adopting A/B testing in practice.
One of the biggest promises of data science is for business owners and executives to be able to understand the causal relationships and fundamental drivers that underpin their business. Find out how A/B tests can be leveraged to do just that and why you should.