Towards Effective Foraging by Data Scientists to Find Past Analysis Choices
| Autor: | Brad A. Myers, Mary Beth Kery, Bonnie E. John, Amber Horvath, Patrick O'Flaherty |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Creative visualization
Computer science Process (engineering) media_common.quotation_subject 05 social sciences Foraging Psychological intervention 020207 software engineering 02 engineering and technology Data science Test (assessment) 0202 electrical engineering electronic engineering information engineering Code (cryptography) 0501 psychology and cognitive sciences 050107 human factors media_common |
| Zdroj: | CHI |
| DOI: | 10.1145/3290605.3300322 |
| Popis: | Data scientists are responsible for the analysis decisions they make, but it is hard for them to track the process by which they achieved a result. Even when data scientists keep logs, it is onerous to make sense of the resulting large number of history records full of overlapping variants of code, output, plots, etc. We developed algorithmic and visualization techniques for notebook code environments to help data scientists forage for information in their history. To test these interventions, we conducted a think-aloud evaluation with 15 data scientists, where participants were asked to find specific information from the history of another person's data science project. The participants succeed on a median of 80% of the tasks they performed. The quantitative results suggest promising aspects of our design, while qualitative results motivated a number of design improvements. The resulting system, called Verdant, is released as an open-source extension for JupyterLab. |
| Databáze: | OpenAIRE |
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