Keeping abreast of current trends, technologies, and best practices in visualization and data analysis is becoming increasingly difficult, especially for fledgling data scientists. 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.

Sensemaking is the iterative process of identifying, extracting, and explaining insights from data, where each iteration is referred to as the “sensemaking loop.” However, little is known about how sensemaking behavior evolves from exploration and explanation during this process. This gap limits our ability to understand the full scope of sensemaking, which in turn inhibits the design of tools that support the process. We contribute the first mixed-method to characterize how sensemaking evolves within computational notebooks. We study 2,574 Jupyter notebooks mined from GitHub by identifying data science notebooks that have undergone significant iterations, presenting a regression model that automatically characterizes sensemaking activity, and using this regression model to calculate and analyze shifts in activity across GitHub versions. Our results show that notebook authors participate in various sensemaking tasks over time, such as annotation, branching analysis, and documentation. We use our insights to recommend extensions to current notebook environments.

Keeping abreast of current trends, technologies, and best practices in visualization and data analysis is becoming increasingly difficult, especially for fledgling data scientists. 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 6,000 Jupyter notebooks. We evaluate Lodestar in a formative study guiding our next set of improvements to the tool. Our results suggest that users find Lodestar useful for rapidly creating data science workflows.