Data analysts use computational notebooks to write code for analyzing and visualizing data.
Notebooks help analysts iteratively write analysis code by letting them interleave code with …

Effective collaboration in data science can leverage domain expertise from each team
member and thus improve the quality and efficiency of the work. Computational notebooks …

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

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

Jupyter notebooks---documents that contain live code, equations, visualizations, and
narrative text---now are among the most popular means to compute, present, discuss and …

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 …

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 …

Computational notebooks, which seamlessly interleave code with results, have become a
popular tool for data scientists due to the iterative nature of exploratory tasks. However …

Causal inference is a statistical paradigm for quantifying causal effects using observational
data. It is a complex process, requiring multiple steps, iterations, and collaborations with …