Data science is a rapidly growing discipline and organizations increasingly depend on data
science work. Yet the ambiguity around data science, what it is, and who data scientists are …

Data analysis is challenging as analysts must navigate nuanced decisions that may yield
divergent conclusions. AI assistants have the potential to support analysts in planning their …

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

The authors' central premise is that a human–LLM (large language model) hybrid approach
leads to efficiency and effectiveness gains in the marketing research process. In qualitative …

This work critically evaluated the applicability of machine learning methodology applied to
automated well log creation towards reliable lithology prediction and subsequent reservoir …

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

Data storytelling plays an important role in data workers' daily jobs since it boosts team
collaboration and public communication. However, to make an appealing data story, data …

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 …

Over the past few years, several software companies have emerged that offer process
mining tools to assist enterprises in gaining insights into their process executions. However …

Data scientists often collaborate with clients to analyze data to meet a client's needs. What
does the end-to-end workflow of a data scientist's collaboration with clients look like …