Data Scientists in Software Teams: State of the Art and Challenges
Data Scientists in Software Teams: State of the Art and Challenges
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DOI：
10.1109/tse.2017.2754374
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发表时间：
2018-11-01
影响因子：
7.4
通讯作者：
Begel, Andrew
中科院分区：
文献类型：
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作者：
Kim, Miryung;Zimmermann, Thomas;Begel, Andrew
软件行业对大规模遥测、机器和质量数据分析的需求正在迅速增长。数据科学家在软件团队中越来越受欢迎，例如，Facebook、linkedin和微软正在为数据科学家创造新的职业道路。在本文中，我们对微软的793名专业数据科学家进行了大规模调查，以了解他们的教育背景、他们研究的问题主题、工具使用和活动。我们根据各种活动所花费的时间对这些数据科学家进行了分类，并确定了9个不同的数据科学家集群，以及他们相应的特征。我们还讨论了他们面临的挑战以及他们与其他数据科学家分享的最佳实践。我们的研究发现了微软软件工程背景下数据科学家的几个趋势，并且应该告知管理人员如何在他们的团队中有效地利用数据科学能力。
The demand for analyzing large scale telemetry, machine, and quality data is rapidly increasing in software industry. Data scientists are becoming popular within software teams, e.g., Facebook, Linkedln and Microsoft are creating a new career path for data scientists. In this paper, we present a large-scale survey with 793 professional data scientists at Microsoft to understand their educational background, problem topics that they work on, tool usages, and activities. We cluster these data scientists based on the time spent for various activities and identify 9 distinct clusters of data scientists, and their corresponding characteristics. We also discuss the challenges that they face and the best practices they share with other data scientists. Our study finds several trends about data scientists in the software engineering context at Microsoft, and should inform managers on how to leverage data science capability effectively within their teams.