Benchmarking AI Performance on End-to-End Data Science Projects
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910023314046976 |
|---|---|
| author | Hughes, Evelyn Alexander, Rohan |
| author_facet | Hughes, Evelyn Alexander, Rohan |
| contents | Data science is an integrated workflow of technical, analytical, communication, and ethical skills, but current AI benchmarks focus mostly on constituent parts. We test whether AI models can generate end-to-end data science projects. To do this we create a benchmark of 40 end-to-end data science projects with associated rubric evaluations. We use these to build an automated grading pipeline that systematically evaluates the data science projects produced by generative AI models. We find the extent to which generative AI models can complete end-to-end data science projects varies considerably by model. Most recent models did well on structured tasks, but there were considerable differences on tasks that needed judgment. These findings suggest that while AI models could approximate entry-level data scientists on routine tasks, they require verification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14284 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Benchmarking AI Performance on End-to-End Data Science Projects Hughes, Evelyn Alexander, Rohan Other Statistics Computers and Society Data science is an integrated workflow of technical, analytical, communication, and ethical skills, but current AI benchmarks focus mostly on constituent parts. We test whether AI models can generate end-to-end data science projects. To do this we create a benchmark of 40 end-to-end data science projects with associated rubric evaluations. We use these to build an automated grading pipeline that systematically evaluates the data science projects produced by generative AI models. We find the extent to which generative AI models can complete end-to-end data science projects varies considerably by model. Most recent models did well on structured tasks, but there were considerable differences on tasks that needed judgment. These findings suggest that while AI models could approximate entry-level data scientists on routine tasks, they require verification. |
| title | Benchmarking AI Performance on End-to-End Data Science Projects |
| topic | Other Statistics Computers and Society |
| url | https://arxiv.org/abs/2602.14284 |