面向智能算法设计与分析的数据科学与大数据技术课程改革与实践

Reform and Practice of Data Science and Big Data Technology Course for Intelligent Algorithm Design and Analysis

摘要: 在数字经济上升为国家战略、全球数据量激增的背景下，针对当前数据科学与大数据技术本科教育存在的理论实践割裂、技术生态脱节、能力评估错位三重结构性矛盾，本文提出以“智能算法设计与分析”为核心的课程改革方案。通过重构“基础–进阶–前沿”三层递进课程体系，强化算法设计思维与性能分析；创新“混合式教学 + 项目驱动”模式贯穿全流程实战；建设集成云环境与算法可视化工具的智能实践平台；建立过程性评价占70%的能力导向考核机制；并依托校企协同提升师资工程素养。

Abstract: Against the backdrop of the digital economy’s elevation to a national strategy and the explosive growth of global data, this paper addresses the three structural fractures plaguing undergraduate programs in Data Science and Big Data Technology: 1) the divorce of theory from practice, 2) the disconnect from the live technology ecosystem, and 3) the misalignment of competency assessment. We propose a curriculum overhaul centered on “Intelligent Algorithm Design and Analysis.” The reform 1) rebuilds a three-tier “foundations-advances-frontiers” course stack that hard-codes algorithmic thinking and performance analysis; 2) embeds a “blended + project-driven” instructional model end-to-end to ensure every concept is battle-tested in production-scale projects; 3) deploys a cloud-native, visualization-rich practice platform that lets students prototype, benchmark, and iterate at the speed of industry; 4) shifts grading to a 70% process-oriented, competency-based rubric; and 5) leverages deep university-industry partnerships to keep faculty engineering instincts razor-sharp.