Workflow Automation and Multi-Kernel Support in Jupyter

This unit introduces tools and techniques for building automated, structured workflows directly within Jupyter notebooks. Participants begin by using PyDoit to define and run custom tasks, enabling reproducible execution of data processing, analysis, and plotting steps. The unit then explores how to manage task dependencies through file tracking, ensuring that only necessary computations are rerun. In addition, participants learn how to expand their Jupyter environment to support multiple programming languages by installing and using alternative kernels such as R and Octave, thereby enhancing flexibility for cross-language research workflows.