生产数据科学内幕:探索生产环境中数据科学家的主要任务

AI Pub Date : 2024-06-12 DOI:10.3390/ai5020043
A. Schmetz, A. Kampker
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引用次数: 0

摘要

现代生产依赖基于数据的分析来预测和优化生产流程。专业数据科学家在公司和研究机构执行任务,处理来自实际生产环境的真实数据。数据预处理和数据质量在数据科学中起着至关重要的作用,而这方面的方法和技术也是一个活跃的研究领域。虽然轶事和一般调查显示预处理是数据科学家的主要业务任务,但缺少对生产数据的子任务和领域的详细了解。在本文中,我们对生产领域的数据科学任务进行了多阶段调查。利用专家知识和洞察力,我们发现数据预处理是数据科学家任务的主要部分。具体而言,我们发现处理缺失值、查找数据点含义以及同步多个时间序列往往是最耗时的预处理任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inside Production Data Science: Exploring the Main Tasks of Data Scientists in Production Environments
Modern production relies on data-based analytics for the prediction and optimization of production processes. Specialized data scientists perform tasks at companies and research institutions, dealing with real data from actual production environments. The roles of data preprocessing and data quality are crucial in data science, and an active research field deals with methodologies and technologies for this. While anecdotes and generalized surveys indicate preprocessing is the major operational task for data scientists, a detailed view of the subtasks and the domain of production data is missing. In this paper, we present a multi-stage survey on data science tasks in practice in the field of production. Using expert knowledge and insights, we found data preprocessing to be the major part of the tasks of data scientists. In detail, we found that tackling missing values, finding data point meanings, and synchronization of multiple time-series were often the most time-consuming preprocessing tasks.
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AI
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