一句话：基于793名微软数据科学家的调查，刻画其背景、工作主题、工具使用与活动聚类，并总结挑战与最佳实践。
软件行业对大规模遥测、机器和质量数据分析的需求迅速增长，数据科学家在软件团队中愈发常见。本文针对微软793名专业数据科学家开展大规模问卷调查，了解其教育背景、所处理的问题主题、工具使用和工作活动。作者依据各类活动的时间投入对受访者进行聚类，识别出9类具有不同特征的数据科学家。文章还讨论他们面临的挑战以及同行分享的最佳实践。研究揭示了微软软件工程语境下数据科学家的若干趋势，并为管理者如何在团队中有效利用数据科学能力提供参考。
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. For example, Face-book, LinkedIn 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.
DOI 原文 ·
@inproceedings{paperbot4520,
title = {Data scientists in software teams},
author = {Miryung Kim and Thomas Zimmermann and Robert DeLine and Andrew Begel},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3182515}
}