Computational notebooks-such as Azure, Databricks, and Jupyter-are a popular, interactive
paradigm for data scientists to author code, analyze data, and interleave visualizations, all …

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 …

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

Data scientists are responsible for the analysis decisions they make, but it is hard for them to
track the process by which they achieved a result. Even when data scientists keep logs, it is …

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 …

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 …

More than ninety percent of published Jupyternotebooks do not state dependencies on
external packages. This makes them non-executable and thus hinders reproducibility of …

By bringing together code, text, and examples, Jupyter notebooks have become one of the
most popular means to produce scientific results in a productive and reproducible way. As …

Drawing reliable inferences from data involves many, sometimes arbitrary, decisions across
phases of data collection, wrangling, and modeling. As different choices can lead to …