Data Makes Better Data Scientists

Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.) Pub Date : 2023-06-18 DOI:10.1145/3597465.3605228

Jinjin Zhao, A. Gal, Sanjay Krishnan

引用次数: 0

Abstract

With the goal of identifying common practices in data science projects, this paper proposes a framework for logging and understanding incremental code executions in Jupyter notebooks. This framework aims to allow reasoning about how insights are generated in data science and extract key observations into best data science practices in the wild. In this paper, we show an early prototype of this framework and ran an experiment to log a machine learning project for 25 undergraduate students.

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数据造就更好的数据科学家

为了确定数据科学项目中的常见实践，本文提出了一个框架，用于在Jupyter笔记本中记录和理解增量代码执行。这个框架的目的是允许推理如何在数据科学中产生洞察力，并将关键观察提取到野外的最佳数据科学实践中。在本文中，我们展示了该框架的早期原型，并为25名本科生运行了一个记录机器学习项目的实验。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. Workshop on Human-In-the-Loop Data Analytics (2nd : 2017 : Chicago, Ill.)

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