The rapid advances of large language models (LLMs), such as ChatGPT, are revolutionizing data science and statistics. These state-of-the-art tools can streamline complex processes. As a result, it reshapes the role of data scientists. We argue that LLMs are transforming the responsibilities of data scientists, shifting their focus from hands-on coding, data-wrangling and conducting standard analyses to assessing and managing analyses performed by these automated AIs. This evolution of roles is reminiscent of the transition from a software engineer to a product manager. We illustrate this transition with concrete data science case studies using LLMs in this paper. These developments necessitate a meaningful evolution in data science education. Pedagogy must now place greater emphasis on cultivating diverse skillsets among students, such as LLM-informed creativity, critical thinking, AI-guided programming. LLMs can also play a significant role in the classroom as interactive teaching and learning tools, contributing to personalized education. This paper discusses the opportunities, resources and open challenges for each of these directions. As with any transformative technology, integrating LLMs into education calls for careful consideration. While LLMs can perform repetitive tasks efficiently, it's crucial to remember that their role is to supplement human intelligence and creativity, not to replace it. Therefore, the new era of data science education should balance the benefits of LLMs while fostering complementary human expertise and innovations. In conclusion, the rise of LLMs heralds a transformative period for data science and its education. This paper seeks to shed light on the emerging trends, potential opportunities, and challenges accompanying this paradigm shift, hoping to spark further discourse and investigation into this exciting, uncharted territory.

翻译：大型语言模型（如ChatGPT）的快速进步正革新数据科学与统计学领域。这些尖端工具能够简化复杂流程，进而重塑数据科学家的角色。我们认为，大型语言模型正在转变数据科学家的职责——从专注于手动编码、数据整理及标准分析，转向评估和管理由自动化人工智能执行的分析任务。这种角色演变类似于软件工程师向产品经理的转型。本文通过使用大型语言模型的具体数据科学案例研究，阐释了这一转变。这些发展要求数据科学教育进行有意义的革新。教学法必须更加注重培养学生的多元技能，例如基于大型语言模型的创造力、批判性思维、人工智能引导的编程能力。此外，大型语言模型还可作为课堂中的交互式教学工具，为个性化教育做出贡献。本文探讨了这些方向各自的机遇、已有资源及待解决的开放挑战。与任何变革性技术类似，将大型语言模型融入教育需要审慎考量。虽然大型语言模型能高效完成重复性任务，但必须牢记其作用是辅助而非取代人类智慧与创造力。因此，新时代的数据科学教育应在发挥大型语言模型优势的同时，培养互补的人类专业能力与创新精神。总之，大型语言模型的崛起预示着数据科学及其教育领域的转型期。本文旨在揭示伴随这一范式转变的新兴趋势、潜在机遇与挑战，期望能为这片令人振奋的未知领域引发更多讨论与探索。