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Keeping abreast of current trends, technologies, and best practices invisualization and data analysis is becoming increasingly difficult, especiallyfor fledgling data scientists. In this paper, we propose Lodestar, aninteractive computational notebook that allows users to quickly explore andconstruct new data science workflows by selecting from a list of automatedanalysis recommendations. We derive our recommendations from directed graphs ofknown analysis states, with two input sources: one manually curated from onlinedata science tutorials, and another extracted through semi-automatic analysisof a corpus of over 6,000 Jupyter notebooks. We evaluate Lodestar in aformative study guiding our next set of improvements to the tool. Our resultssuggest that users find Lodestar useful for rapidly creating data scienceworkflows.

Lodestar: Supporting Independent Learning and Rapid Experimentation Through Data-Driven Analysis Recommendations