By writing our data science labs in Jupyter Notebook and hosting them in, we’re aiming to bring our students the best of both worlds. It combines Jupyter’s interactive coding and visualization environment with Learn’s test-driven development framework and learning support features. 

Here’s some tips on how to develop, test, and debug in our unique combined environment:

  • Code along in our Jupyter Notebook Introduction lesson to learn the Jupyter workflow.

  • Your Jupyter edits are automatically saved to your personal Github fork.

  • If you ever mess up a Jupyter lesson irrevocably, you can delete your personal Github fork of that lesson and reload the lesson to recover a fresh copy.

  • Jupyter labs (lessons that require you to “Run Tests” before you can progress) apply test-driven coding paradigms to the Jupyter notebook. When you’ve finished working through the notebook, Click “Run Tests” to validate your work.

  • If your tests fail, debug through the test output, and double-check the test directory by clicking this icon:

If the lesson content doesn’t seem to load properly - you can always try refreshing the page!

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