  Computational notebooks such as Jupyter are popular for exploratory data
analysis and insight finding. Despite the module-based structure, notebooks
visually appear as a single thread of interleaved cells containing text, code,
visualizations, and tables, which can be unorganized and obscure users' data
analysis workflow. Furthermore, users with limited coding expertise may
struggle to quickly engage in the analysis process. In this work, we design and
implement an interactive notebook framework, WHATSNEXT, with the goal of
supporting low-code visual data exploration with insight-based user guidance.
In particular, we (1) re-design a standard notebook cell to include a
recommendation panel that suggests possible next-step exploration questions or
analysis actions to take, and (2) create an interactive, dynamic tree
visualization that reflects the analytic dependencies between notebook cells to
make it easy for users to see the structure of the data exploration threads and
trace back to previous steps.
