  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.
