Today, the prominence of data science within organizations has given rise to teams of data
science workers collaborating on extracting insights from data, as opposed to individual data …

The development of AI applications is a multidisciplinary effort, involving multiple roles
collaborating with the AI developers, an umbrella term we use to include data scientists and …

Unit visualizations have been widely used in data storytelling within interactive articles and
videos. However, authoring data stories that contain animated unit visualizations is …

Data scientists often have to use other presentation tools (eg, Microsoft PowerPoint) to
create slides to communicate their analysis obtained using computational notebooks. Much …

Computational notebooks allow data scientists to express their ideas through a combination
of code and documentation. However, data scientists often pay attention only to the code …

Creating presentation slides is a critical but time-consuming task for data scientists. While
researchers have proposed many AI techniques to lift data scientists' burden on data …

Data science and machine learning (DS/ML) are at the heart of the recent advancements of
many Artificial Intelligence (AI) applications. There is an active research thread in AI,\autoai …

User experience (UX) has undergone a revolution in collaborative practices, due to tools
that enable quick feedback and continuous collaboration with a varied team across a …

Computational notebooks such as Jupyter are now used by millions of data scientists,
machine learning engineers, and computational researchers to do exploratory and end-user …

Data scientists face a steep learning curve in understanding a new domain for which they
want to build machine learning (ML) models. While input from domain experts could offer …