数据科学教育应如何应对大型语言模型？
What Should Data Science Education Do with Large Language Models?
AI总结：
本文探讨LLMs如何重塑数据科学家角色并推动数据科学教育变革，强调应培养学生LLM相关创造力、批判性思维和AI引导编程能力，同时平衡LLM优势与人类专业知识。
摘要：
大型语言模型（LLMs）如ChatGPT的快速发展正在彻底改变数据科学与统计学。这些前沿工具能够简化复杂流程，从而重塑数据科学家的角色。我们认为，LLMs正在转变数据科学家的职责，使其重心从亲自动手编写代码、数据整理和执行标准分析，转向评估和管理由这些自动化人工智能所完成的分析。这种角色演变类似于从软件工程师到产品经理的转变。本文通过使用LLMs的具体数据科学案例研究来说明这一转变。这些发展要求数据科学教育进行有意义的变革。教学法现在必须更加注重培养学生多样化的技能组合，例如基于LLM的创造力、批判性思维和AI引导的编程。LLMs还可以作为互动式教学工具在课堂中发挥重要作用，助力个性化教育。本文讨论了上述每个方向所面临的机会、资源和开放性挑战。与任何变革性技术一样，将LLMs融入教育需要审慎考量。虽然LLMs能够高效执行重复性任务，但必须牢记其作用是补充而非取代人类的智慧与创造力。因此，数据科学教育的新时代应在发挥LLMs优势的同时，培养互补的人类专业知识与创新能力。总之，LLMs的兴起预示着数据科学及其教育进入一个变革时期。本文旨在阐明伴随这一范式转变而出现的新兴趋势、潜在机遇与挑战，以期激发对这一令人振奋且尚未开拓领域的进一步讨论与研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
