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.

大型语言模型 (Large Language Models, LLMs) 的迅猛发展正在革新数据科学和统计学，该论文旨在探讨 LLMs 对于数据科学教育的潜在机遇、资源和挑战，以及其在数据科学中的作用转变和创造性应用。

数据科学教育应如何处理大型语言模型？

What Should Data Science Education Do with Large Language Models?

Data science is an interdisciplinary research area where scientists are
typically working with data coming from different fields. When using and
analyzing data, the scientists implicitly agree to follow standards,
procedures, and rules set in these fields. However, guidance on the
responsibilities of the data scientists and the other involved actors in a data
science project is typically missing. While literature shows that novel
frameworks and tools are being proposed in support of open-science, data reuse,
and research data management, there are currently no frameworks that can fully
express responsibilities of a data science project. In this paper, we describe
the Transparency, Accountability, Privacy, and Societal Responsibility Matrix
(TAPS-RM) as framework to explore social, legal, and ethical aspects of data
science projects. TAPS-RM acts as a tool to provide users with a holistic view
of their project beyond key outcomes and clarifies the responsibilities of
actors. We map the developed model of TAPS-RM with well-known initiatives for
open data (such as FACT, FAIR and Datasheets for datasets). We conclude that
TAPS-RM is a tool to reflect on responsibilities at a data science project
level and can be used to advance responsible data science by design.

本研究提出了一个名为 TAPS-RM 的框架，该框架可以通过社会、法律和伦理的视角探索数据科学项目的方方面面，并明确参与者的责任。TAPS-RM 是推动负责任的数据科学的设计的工具。

TAPS 责任矩阵：一种用于负责任的数据科学设计的工具

TAPS Responsibility Matrix: A tool for responsible data science by design

We study Sinkhorn EM (sEM), a variant of the expectation maximization (EM)
algorithm for mixtures based on entropic optimal transport. sEM differs from
the classic EM algorithm in the way responsibilities are computed during the
expectation step: rather than assign data points to clusters independently, sEM
uses optimal transport to compute responsibilities by incorporating prior
information about mixing weights. Like EM, sEM has a natural interpretation as
a coordinate ascent procedure, which iteratively constructs and optimizes a
lower bound on the log-likelihood. However, we show theoretically and
empirically that sEM has better behavior than EM: it possesses better global
convergence guarantees and is less prone to getting stuck in bad local optima.
We complement these findings with experiments on simulated data as well as in
an inference task involving C. elegans neurons and show that sEM learns cell
labels significantly better than other approaches.

通过熵优化传输的期望最大化算法对混合模型进行研究，该算法通过结合有关混合权重的先验信息来计算职责，具有优于经典期望最大化算法的全局收敛性保证和较少陷入坏局的优越性能，这在模拟数据和 C. elegans 神经元的细胞标记方面的实验得到了证实。