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Self-guided Curriculum |
This page outlines an ordered list of lecture materials, recordings, assignments, and online resources curated to give burgeoning data scientists a primer in the best practices for sustainable, production-grade software development.
These resources are best utilized in a reverse-classroom format, where you move through them at your own pace but have access to regular office hours and work periods with course instructors and experienced engineering mentors. If you have this arrangement, try the exercises contained in these lessons on your own time and come to instructor-led sessions with questions ready.
For more information about arranging help sessions for this content, please direct requests to the University of Washington's eScience Studio.
Please follow these instructions for setting up the software environment assumed by the following resources.
- The UNIX shell (Software Carpentries lesson)
- Extra resources: Learning Linux Commands
- Version control with Git (Software Carpentries lesson)
- Python Fundamentals (Software Carpentries lesson)
- Extra resources: A Whirlwind Tour of Python, Real Python on imports
- Using Pandas for big data (notebook)
- Extra resources: Python Data Science Handbook
- When things go wrong: Debugging, Exceptions, Testing
- Visualization with Altair
- Packaging Python code for distribution
- Designing software, designing use cases
- Technology review, Standups
- Code Hygiene: Documentation and Style
- Slides
- Video
- Demo code
- Extra resources: PEP8, Google Python Style Guide
- Virtual environments, Continuous Integration
- Communication