Imagine giving a powerful AI a toolbox and asking it to solve complex data science problems. That's the potential of Large Language Models (LLMs) equipped with code interpreter plugins. But how do we truly gauge their effectiveness? Researchers have developed a clever benchmarking framework called CIBench, designed to push these AI agents to their limits. CIBench simulates real-world data science workflows, presenting the LLM with a series of interconnected questions within an interactive coding environment. This approach mimics how data scientists tackle projects, iteratively refining their code based on results. The framework tests different Python libraries like Pandas for data manipulation, Matplotlib for visualizations, and even more complex tools like PyTorch for machine learning. Two key testing modes are used: "end-to-end," where the LLM works independently, and "oracle," where it receives guidance when stuck, much like a junior data scientist learning from a mentor. This reveals not only the LLM's autonomous problem-solving skills but also its ability to learn from human input. Early tests with CIBench show that while open-source LLMs are making strides, they still struggle with intricate tasks, especially those involving modeling and complex reasoning. Interestingly, LLMs demonstrate significant improvement when allowed multiple attempts to solve a problem, highlighting their potential for self-correction. The future of data science might just be a collaborative partnership between humans and these powerful AI assistants. As LLMs get better at understanding instructions, reasoning through code, and integrating human feedback, they'll transform the way we analyze data and unlock new possibilities.