Casual Notebooks and Rigid Scripts: Understanding Data Science Programming

Data workers are non-professional data scientists who often use scripting languages like R, Python, or MATLAB, and employ an exploratory programming workflow. Current IDEs offer them two main programming modalities: script files and computational notebooks. To understand how these modalities impact work practice, we conducted a study with 21 data workers, and a subsequent larger survey with 62 respondents. Through interviews, walkthroughs, and screen recordings, we collected information about their workflows. Our analysis shows a tension between scripts and computational notebooks. Scripts are more common, better support storage and execution of previous analyses, but hinder experimentation. Notebooks better suit the actual data science workflow, but can become easily unorganized. We discuss how this dual nature of modality usage leads to several issues that affect data workers’ workflows, and discuss implications for the design of programming IDEs.