# Using ChatGPT for Data Science Analyses

arXiv: https://arxiv.org/abs/2404.08480 · HTML: https://mlsanity.com/paper/2404.08480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries](https://mlsanity.com/paper/2312.13671/) (2023)
- [Automating Data Science: Prospects and Challenges](https://mlsanity.com/paper/2105.05699/) (2021)
- [k-POD: A Method for k-Means Clustering of Missing Data](https://mlsanity.com/paper/1411.7013/) (2014)
- [What Should Data Science Education Do with Large Language Models?](https://mlsanity.com/paper/2307.02792/) (2023)

## Abstract (arXiv)

As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI's Data Analysis plugin. While it offers powerful support as a quantitative co-pilot, its limitations demand careful consideration in empirical analysis. This paper assesses the potential of ChatGPT for data science analyses, illustrating its capabilities for data exploration and visualization, as well as for commonly used supervised and unsupervised modeling tasks. While we focus here on how the Data Analysis plugin can serve as co-pilot for Data Science workflows, its broader potential for automation is implicit throughout.