We characterize five task sequences related to visualizing dimensionally-reduced data,
drawing from data collected from interviews with ten data analysts spanning six application …

Despite the widespread adoption of computational notebooks, little is known about best
practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a …

Most visualizations today are produced by retrieving data from a database and using a
specialized visualization tool to render it. This decoupled approach results in significant …

Uncertainty plays an important and complex role in data analysis, where the goal is to find
pertinent patterns, build robust models, and support decision making. While these …

Generating knowledge from data is an increasingly important activity. This process of data
exploration consists of multiple tasks: data ingestion, visualization, statistical analysis, and …

Data analysis is challenging as analysts must navigate nuanced decisions that may yield
divergent conclusions. AI assistants have the potential to support analysts in planning their …

Dashboards are the ubiquitous means of data communication within organizations. Yet we
have limited understanding of how they factor into data practices in the workplace …

This textbook presents the main principles of visual analytics and describes techniques and
approaches that have proven their utility and can be readily reproduced. Special emphasis …

Duplicating one's own code makes it faster to write software. This expediency is particularly
valuable for users of computational notebooks. Duplication allows notebook users to quickly …

Computational notebooks aim to support collaborative data analysis by combining code,
visualizations, and text in a single easily shared document. Yet, as notebooks evolve and …