学术报告（李元章 2025.11.3）

摘要：Artificial intelligence (AI) has grown exponentially in recent years, fueled by advances in data availability and computing power. As AI moves beyond hype toward high-value applications—and as quantum computing promises to accelerate this trajectory—the fields of statistics and data science play a pivotal role in shaping its future. Statisticians provide the methodological rigor required to analyze data, validate models, and ensure interpretability, while data science extends these foundations through computational tools, domain expertise, and real-time decision-making applications. This paper examines the intertwined development of statistics and data science, highlighting their overlapping skills, divergent emphases, and complementary contributions. We argue that statistics remains the theoretical backbone of data science, supporting areas such as causal inference, experimental design, and model transparency. At the same time, data science has expanded the scope of statistics by integrating machine learning, predictive analytics, and large-scale computing. We also propose a framework for modernizing statistics education—at the undergraduate, master’s, and doctoral levels—by incorporating data science, machine learning, ethics, and interdisciplinary applications to prepare students for a future shaped by AI. Ultimately, the future of statistics lies not in being replaced by data science, but in evolving alongside it to meet the demands of an AI-driven world.