How do Data Science Workers Collaborate? Roles, Workflows, and Tools

Author:

Zhang Amy X.1,Muller Michael2,Wang Dakuo3

Affiliation:

1. University of Washington & Massachusetts Institute of Technology, Seattle, WA, USA

2. IBM Research, Cambridge, MA, USA

3. IBM Research & MIT-IBM Watson AI Lab, Cambridge, MA, USA

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)

Cited by 161 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Navigating the Future;Advances in Business Information Systems and Analytics;2024-06-14

2. Couler: Unified Machine Learning Workflow Optimization in Cloud;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. OutlineSpark: Igniting AI-powered Presentation Slides Creation from Computational Notebooks through Outlines;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

4. Guidelines for Integrating Value Sensitive Design in Responsible AI Toolkits;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

5. Bitacora: A Toolkit for Supporting NonProfits to Critically Reflect on Social Media Data Use;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3