Photo of Souti Chattopadhyay
Souti Chattopadhyay, graduate student of computer science.

Souti Chattopadhyay, graduate student of computer science in the College of Engineering at Oregon State University, was first author on a paper that won the Honorable Mention Award at the 2020 ACM CHI Conference on Human Factors in Computing Systems. The distinction is given to the top 10% of the papers presented.

Other authors include her advisor, Anita Sarma, associate professor of computer science, and colleagues at Microsoft and University of Tennessee-Knoxville.

“This award means that our research matters and provides deeper insight into what the future can hold in terms of accessible and inclusive computing,” Chattopadhyay said.

Chattopadhyay’s research examines how data scientists make decisions when interacting with programming interfaces. The goal is to make programming tools contextually assistive with freedom to delay and review the outcomes of decisions along the path.

What’s Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities

Souti Chattopadhyay1, Ishita Prasad2, Austin Z. Henley3, Anita Sarma1, Titus Barik2

Oregon State University1, Microsoft2, University of Tennessee-Knoxville3


Computational notebooks—such as Azure, Databricks, and Jupyter—are a popular, interactive paradigm for data scientists to author code, analyze data, and interleave visualizations, all within a single document. Nevertheless, as data scientists incorporate more of their activities into notebooks, they encounter unexpected difficulties, or pain points, that impact their productivity and disrupt their workflow. Through a systematic, mixed-methods study using semi-structured interviews (n = 20) and survey (n = 156) with data scientists, we catalog nine pain points when working with notebooks. Our findings suggest that data scientists face numerous pain points throughout the entire workflow—from setting up notebooks to deploying to production—across many notebook environments. Our data scientists report essential notebook requirements, such as supporting data exploration and visualization. The results of our study inform and inspire the design of computational notebooks.

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