2020/01/18 by Amy X. Zhang, Michael Müller, Zhang, Amy X. +4 · 13 citations
Business, Management and Accounting · Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Software Engineering (cs.SE) #Software Engineering Research #Software Engineering Techniques and Practices #cs.AI #cs.HC #cs.LG #cs.SE #stat.ML
paper · pdf · doi:10.48550/arxiv.2001.06684
CSCW'2020
openalex publication_date 2020/01/18 · arxiv created 2020/04/16 · arxiv updated 2020/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). 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 also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.