Exploratory Data Analysis (EDA) is an important initial step for any knowledge discovery
process, in which data scientists interactively explore unfamiliar datasets by issuing a …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component
of contemporary coding contexts. Among these environments, computational notebooks …

The potential for machine learning (ML) systems to amplify social inequities and unfairness
is receiving increasing popular and academic attention. A surge of recent work has focused …

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One
application domain is data science. New techniques in automating the creation of AI, known …

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 …

Jupyter Notebooks have been widely adopted by many different communities, both in
science and industry. They support the creation of literate programming documents that …

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 wrangling is a difficult and time-consuming activity in computational notebooks, and
existing wrangling tools do not fit the exploratory workflow for data scientists in these …