LiqiangJing/DSBench
[ICLR 2025] DSBench: How Far are Data Science Agents from Becoming Data Science Experts?
This project helps researchers and developers evaluate the performance of 'data science agents' — AI systems designed to perform data analysis and modeling. You input task instructions (which can include images and tables) and raw data files. The system then assesses how well the agent generates a solution to the given data science challenge, providing a benchmark for comparison. This is primarily for AI researchers and developers building or studying data science agents.
108 stars. No commits in the last 6 months.
Use this if you are developing or evaluating AI-driven data science agents and need a standardized way to measure their capability on realistic data tasks.
Not ideal if you are an end-user looking for a tool to perform data analysis directly, as this is a framework for evaluating other AI systems.
Stars
108
Forks
10
Language
Jupyter Notebook
License
—
Category
Last pushed
Aug 17, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/llm-tools/LiqiangJing/DSBench"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Featured in
Higher-rated alternatives
sierra-research/tau2-bench
τ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment
xlang-ai/OSWorld
[NeurIPS 2024] OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
bigcode-project/bigcodebench
[ICLR'25] BigCodeBench: Benchmarking Code Generation Towards AGI
THUDM/AgentBench
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
scicode-bench/SciCode
A benchmark that challenges language models to code solutions for scientific problems