Data analysis can be complex, requiring specialized skills in statistics, programming, and data science. But what if you could analyze data simply by asking questions in plain English? Recent research on Large Language Model-based agents (LLMs) is making this a reality. These “data agents” are revolutionizing how we interact with data, lowering the barrier to entry for non-experts. Imagine typing a request like, “Show me the sales trends in the Northeast, and create a chart.” Data agents can interpret these instructions, access the relevant data, perform the analysis, and even generate visualizations and reports—all automatically. This emerging field is rapidly evolving, with new agents and frameworks constantly being developed. Some, like ChatGPT-Advanced Data Analysis, operate through conversations, allowing users to refine their requests and explore data iteratively. Others, like the Data Interpreter, take a more end-to-end approach, executing a complete analysis based on a single prompt. Researchers are exploring innovative techniques like planning, reasoning, and reflection to make these agents smarter and more autonomous. For example, hierarchical planning breaks down complex tasks into smaller, manageable steps, while reflection helps agents learn from their mistakes and improve their performance. Multi-agent collaboration, where different agents specialize in specific tasks, is also showing promise. While the potential is immense, challenges remain. Current LLMs still struggle with advanced statistical reasoning and multi-modal data (like images and tables). Building robust systems that can handle the complexities of real-world data analysis requires ongoing research and development. The future of data science may well be conversational. As data agents become more sophisticated, they could empower everyone—from business analysts to scientists—to unlock the insights hidden within their data, ultimately democratizing access to powerful analytical tools.