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In this paper, we propose lodestar, an interactive computational notebook that allows users to quickly explore and construct new data science workflows by selecting from a list of automated analysis recommendations. We derive our recommendations from directed graphs of known analysis states, with two input sources: one manually curated from online data science tutorials, and another extracted through semi-automatic analysis of a corpus of over 6000 Jupyter notebooks. We validated Lodestar through three separate user studies: first a formative evaluation involving novices learning data science using the tool. We used the feedback from this study to improve the tool. This was followed by a summative study involving both new and returning participants from the formative evaluation to test the efficacy of our improvements. We also engaged professional data scientists in an expert review assessing the utility of the different recommendations. Overall, our results suggest that both novice and professional users find Lodestar useful for rapidly creating data science workflows.<\/jats:p>","DOI":"10.1177\/14738716231190429","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T06:04:37Z","timestamp":1691993077000},"page":"21-39","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Lodestar: Supporting rapid prototyping of data science workflows through data-driven analysis recommendations"],"prefix":"10.1177","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8372-544X","authenticated-orcid":false,"given":"Deepthi","family":"Raghunandan","sequence":"first","affiliation":[{"name":"University of Maryland, College Park, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Cui","sequence":"additional","affiliation":[{"name":"Google, Inc., Mountain View, CA, 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