Jupyter notebooks provide a useful environment for interactive exploration of data. A common question I get, though, is how you can progress from this nonlinear, interactive, trial-and-error style of exploration to a more linear and reproducible analysis based on organized, packaged, and tested code. This series of videos presents a case study in how I personally approach reproducible data analysis within the Jupyter notebook.

Each video is approximately 5-8 minutes; the videos are available in a YouTube Playlist. Alternatively, below you can find the videos with some description and links to relevant resources