Data Makes Better Data Scientists

TL;DR

Data Makes Better Data Scientists addresses how to quantify data science productivity by logging incremental notebook executions to reveal how insights are generated. The authors propose a three-step framework: log edits to create a timeline, segment logs into mini-processes that capture data insights, and extract KPIs for cross-project comparisons; they demonstrate an early prototype with 25 undergraduates performing a ML task. The work highlights potential applications in informative user studies, cross-domain workflow analysis, data governance, and education by analyzing workflow differences and providing targeted feedback. This approach can support better curriculum design, governance policies, and empirical understanding of practical data science workflows.

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.

Figures (2)

• Figure 1: Architecture of proposed Jupyter notebook log framework
• Figure 2: Example of execution log generated from a computational notebook