Large Language Models in the Data Science Lifecycle: A Systematic Mapping Study

TL;DR

This systematic mapping study reviews how Large Language Models are applied across the data science lifecycle, highlighting current trends, challenges, and opportunities for future research in this rapidly evolving field.

Contribution

It provides a comprehensive categorization and analysis of LLM applications, evaluation methods, and limitations within the data science workflow.

Findings

Identification of key LLM types used in data science

Analysis of evaluation metrics across studies

Documentation of limitations and future research gaps

Abstract

In recent years, Large Language Models (LLMs) have emerged as transformative tools across numerous domains, impacting how professionals approach complex analytical tasks. This systematic mapping study comprehensively examines the application of LLMs throughout the Data Science lifecycle. By analyzing relevant papers from Scopus and IEEE databases, we identify and categorize the types of LLMs being applied, the specific stages and tasks of the data science process they address, and the methodological approaches used for their evaluation. Our analysis includes a detailed examination of evaluation metrics employed across studies and systematically documents both positive contributions and limitations of LLMs when applied to data science workflows. This mapping provides researchers and practitioners with a structured understanding of the current landscape, highlighting trends, gaps, and opportunities for future research in this rapidly evolving intersection of LLMs and data science.