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 …

Business Intelligence (BI) is known to make smart decisions in various fields. BI proves
beneficial for better-visualising of data through reports, charts, ad-hoc queries, dashboards …

Interfaces for machine learning (ML), information and visualizations about models or data,
can help practitioners build robust and responsible ML systems. Despite their benefits …

Immersive analytics turns the very space surrounding the user into a canvas for data
analysis, supporting human cognitive abilities in myriad ways. We present the results of a …

Data scientists have embraced computational notebooks to author analysis code and
accompanying visualizations within a single document. Currently, although these media …

Exploratory data science largely happens in computational notebooks with dataframe APIs,
such as pandas, that support flexible means to transform, clean, and analyze data. Yet …

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 …

When an organization chooses one course of action over alternatives, this task typically falls
on a decision maker with relevant knowledge, experience, and understanding of context …

Effective data analysis ideally requires the analyst to have high expertise as well as high
knowledge of the data. Even with such familiarity, manually pursuing all potential …

Analyzing complex data is a non‐linear process that alternates between identifying discrete
facts and developing overall assessments and conclusions. In addition, data analysis rarely …