HCI engages with data science through many topics and themes. Researchers have
addressed biased dataset problems, arguing that bad data can cause innocent software to …

There has been substantial growth in the use of JSON-based grammars, as well as other
standard data serialization languages, to create visualizations. Each of these grammars …

Statistical methods generally have assumptions (eg, normality in linear regression models).
Violations of these assumptions can cause various issues, like statistical errors and biased …

Eliciting knowledge from domain experts can play an important role throughout the machine
learning process, from correctly specifying the task to evaluating model results. However …

Understanding how helpful a visualization is from experimental results is difficult because
the observed performance is confounded with aspects of the study design, such as how …

“Correlation does not imply causation'is a famous mantra in statistical and visual analysis.
However, consumers of visualizations often draw causal conclusions when only correlations …

Visual analytics (VA) tools support data exploration by helping analysts quickly and
iteratively generate views of data which reveal interesting patterns. However, these tools …

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual
analytics can be especially useful. However, the ad hoc nature of exploratory analysis can …

The book equips students with the end-to-end skills needed to do data science. That means
gathering, cleaning, preparing, and sharing data, then using statistical models to analyse …

Dashboards are no longer mere static displays of metrics; through functionality such as
interaction and storytelling, they have evolved to support analytic and communicative goals …