What is this page? This page shows tables extracted from arXiv papers on the left-hand side. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions. We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model).
We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). In this work, we conducted an online survey with 183 participants who work in various aspects of data science. However, we still lack a deep understanding of how data science workers collaborate in practice. Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. How do Data Science Workers Collaborate? Roles, Workflows, and Tools
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