Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and futur...Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.展开更多
以“AI for Science”为代表的智能技术与科学研究的耦合正深刻地改变着常规科学的研究进程,并引发一场关于科学研究的范式革命。本文以科学范式的演进为视角,结合大语言模型在当前科研流程中的核心应用,提出了智能科学范式的概念,并讨...以“AI for Science”为代表的智能技术与科学研究的耦合正深刻地改变着常规科学的研究进程,并引发一场关于科学研究的范式革命。本文以科学范式的演进为视角,结合大语言模型在当前科研流程中的核心应用,提出了智能科学范式的概念,并讨论了人工智能作为媒介在科学研究中的功能统合作用和跨学科的知识整合价值。本文认为,智能科学范式的基本内容包含功用层面的效率提升与理解增强,研究层面的边界突破与能力跃升,思维层面的认知拓展与主体性觉醒。作为科学研究的“第五范式”,智能科学范式的意义体现为跨学科的价值连接、对研究路径的统合以及深度人机协作下的主体强化,分别对应了人工智能在研究对象、研究方法和研究主体上对于科学研究的变革性影响。从科学研究到产业实践,随着人工智能与社会各个领域的深度融合,将会改变社会千行百业的生长逻辑和内部结构,并推动社会全实践领域实现更加智能化、高效化和可持续化的生产和发展。展开更多
文摘Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.
文摘以“AI for Science”为代表的智能技术与科学研究的耦合正深刻地改变着常规科学的研究进程,并引发一场关于科学研究的范式革命。本文以科学范式的演进为视角,结合大语言模型在当前科研流程中的核心应用,提出了智能科学范式的概念,并讨论了人工智能作为媒介在科学研究中的功能统合作用和跨学科的知识整合价值。本文认为,智能科学范式的基本内容包含功用层面的效率提升与理解增强,研究层面的边界突破与能力跃升,思维层面的认知拓展与主体性觉醒。作为科学研究的“第五范式”,智能科学范式的意义体现为跨学科的价值连接、对研究路径的统合以及深度人机协作下的主体强化,分别对应了人工智能在研究对象、研究方法和研究主体上对于科学研究的变革性影响。从科学研究到产业实践,随着人工智能与社会各个领域的深度融合,将会改变社会千行百业的生长逻辑和内部结构,并推动社会全实践领域实现更加智能化、高效化和可持续化的生产和发展。
