Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and
yet our understanding of the process is arguably vague and piecemeal. We contribute a …

Data science is a rapidly growing discipline and organizations increasingly depend on data
science work. Yet the ambiguity around data science, what it is, and who data scientists are …

Visualization recommender systems aim to lower the barrier to exploring basic visualizations
by automatically generating results for analysts to search and select, rather than manually …

Visualizations have a potentially enormous influence on how data are used to make
decisions across all areas of human endeavor. However, it is not clear how this power …

© 2018 VLDB Endowment. In order to democratize data science, we need to fundamentally
rethink the current analytics stack, from the user interface to the “guts.“Most importantly …

Multiverse analysis is an approach to data analysis in which all “reasonable” analytic
decisions are evaluated in parallel and interpreted collectively, in order to foster robustness …

How do analysis goals and context affect exploratory data analysis (EDA)? To investigate
this question, we conducted semi-structured interviews with 18 data analysts. We …

Dirty data and deceptive design practices can undermine, invert, or invalidate the purported
messages of charts and graphs. These failures can arise silently: a conclusion derived from …

Knowledge of human perception has long been incorporated into visualizations to enhance
their quality and effectiveness. The last decade, in particular, has shown an increase in …

Chart construction errors, such as truncated axes or inexpressive visual encodings, can
hinder reading a visualization, or worse, imply misleading facts about the underlying data …