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

Computational notebooks are widely used for data analysis. Their interleaved displays of
code and execution results (eg, visualizations) are welcomed since they enable iterative …

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 …

Profiling data by plotting distributions and analyzing summary statistics is a critical step
throughout data analysis. Currently, this process is manual and tedious since analysts must …

Large language models (LLMs) support data analysis through conversational user
interfaces, as exemplified in OpenAI's ChatGPT (formally known as Advanced Data Analysis …

Data analysis is challenging as it requires synthesizing domain knowledge, statistical
expertise, and programming skills. Assistants powered by large language models (LLMs) …

Abstract ydata-profiling is an open-source Python package for advanced exploratory data
analysis that enables users to generate data profiling reports in a simple, fast, and efficient …

Multi-stage programming tutorials are key learning resources for programmers, using
progressive incremental steps to teach them how to build larger software systems. A good …

The documentation practice for machine-learned (ML) models often falls short of established
practices for traditional software, which impedes model accountability and inadvertently …

Visualization grammars, often based on the Grammar of Graphics (GoG), have much
potential for augmenting data analysis in a programming environment. However, we do not …