%0 Journal Article
%T 物流数据分析与AI算法应用课程探索与思考<br>Exploration and Thinking of Logistics Data Analysis and AI Algorithm Application Course
%A 邓世果
%A 李程
%J Advances in Social Sciences
%P 722-729
%@ 2169-2564
%D 2024
%I Hans Publishing
%R 10.12677/ass.2024.13121159
%X 本课程旨在培养学生掌握物流领域的数据分析技能和AI算法应用能力&#65292;注重理论与实践的结合。课程内容涵盖数据挖掘、机器学习、优化算法等前沿技术&#65292;结合实际物流场景应用&#65292;帮助学生解决复杂物流问题。通过案例研究和项目驱动&#65292;强化学生的实践操作能力。课程的主要建设目标是培养复合型人才&#65292;提升学生的实践能力和就业竞争力&#65292;确保课程内容紧跟物流行业的发展需求。课程内容包括基础理论、数据分析、AI算法及其在物流中的应用等模块&#65292;利用数字化教材和在线平台支持自主学习&#65292;提供丰富的实际案例供学生实践。此外&#65292;采用混合式教学模式&#65292;通过线上自学与线下讨论、案例分析相结合&#65292;提高学生的互动参与度和综合应用能力。评估体系涵盖过程性与终结性评估&#65292;帮助学生不断改进学习效果。课程将加强与企业的合作&#65292;提供更多实习机会&#65292;确保学生能够应对快速发展的技术和行业需求。<br>This course is designed to equip students with data analysis skills and AI algorithm application abilities in the field of logistics, with a strong emphasis on the integration of theory and practice. The curriculum covers advanced technologies such as data mining, machine learning, and optimization algorithms, and applies them to real-world logistics scenarios to help students solve complex logistics problems. Through case studies and project-driven learning, the course aims to strengthen students&#8217; practical skills. The main objective of the course is to develop well-rounded professionals, enhance students&#8217; practical abilities and boost their employability by ensuring that the course content aligns with the evolving needs of the logistics industry. The curriculum includes modules on foundational theories, data analysis, AI algorithms, and their applications in logistics, supported by digital textbooks and online platforms to facilitate independent learning. A rich array of practical cases is provided to allow students to gain hands-on experience. The course adopts a blended learning approach, combining online self-study with in-person discussions and case analysis to increase student engagement and enhance their ability to apply knowledge comprehensively. The assessment system includes both formative and summative evaluations, enabling continuous improvement in student learning outcomes. Additionally, the course will strengthen partnerships with enterprises, offering more internship opportunities to ensure that students are prepared to meet the fast-evolving technological and industry demands.
%K 物流数据分析&#65292
%K AI算法应用&#65292
%K 优化算法&#65292
%K 案例驱动学习<br>Logistics Data Analysis
%K AI Algorithm Applications
%K Optimization Algorithms
%K Case-Driven Learning
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=103996