Wrap Up

Reproducible and Trustworthy Workflows for Data Science

Welcome
Introduction
- 1 How do reproducible and trustworthy workflows impact data science?
- 2 Introduction to the Bash Shell
Version Control with Git
Collaborating on GitHub
Project Organization
- 14 Filenames and data science project organization, Integrated development environments
Virtual Environments
Containers
Data Validation
Scripts and Reports
- 29 Non-interactive scripts
- 30 Reproducible reports: Quarto
Pipelines
- 31 Data analysis pipelines with scripts
- 32 Data analysis pipelines with GNU Make
Software Testing for Data Science
Packaging
Testing Packages
- 45 Package testing: Python (pytest)
- 46 Package coverage: Python
Continuous Integration
Package Documentation
Continuous Deployment and Publishing
Licenses and Copyright
- 62 Copyright and licenses
Wrap Up
- 63 Workflows for reproducible and trustworthy data science wrap-up
Appendix
Archive

Wrap Up

Finally, we review what makes data science reproducible and trustworthy, and use it to critique data analysis projects.

62 Copyright and licenses

63 Workflows for reproducible and trustworthy data science wrap-up