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Data Science Problems

Introduced by Shubham Chandel et al. in Training and Evaluating a Jupyter Notebook Data Science Assistant30 Jan 2022 archive 2025-07-28

Evaluate a natural language code generation model on real data science pedagogical notebooks! Data Science Problems (DSP) includes well-posed data science problems in Markdown along with unit tests to verify correctness and a Docker environment for reproducible execution. About 1/3 of notebooks in this benchmark also include data dependencies, so this benchmark not only can test a model's ability to chain together complex tasks, but also evaluate the solutions on real data! See our paper Training and Evaluating a Jupyter Notebook Data Science Assistant for more details about state of the art results and other properties of the dataset.

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 7 papers for it but never published that list.

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

MIT

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • Data Science Problems

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections