授课教师：Yiru Zhang

国籍：中国

职称：副教授

教师简介（中英文）：张逸儒，分别于2013年、2015年和2020年在中国华中科技大学、法国索邦大学和法国雷恩大学获得学士、硕士和博士学位。

他目前是法国巴黎赛尔吉大学(Université CY Paris-Cergy)副教授，也是法国机电特殊大学（ESME）的外聘教师，负责计算机专业的算法和大数据相关课程的教学工作。他同时也是法国国家科学研究中心（CNRS）实验室ETIS的副研究员，研究兴趣涵盖了基于信任函数理论的不完全信息推理、决策理论和时序数据挖掘算法等，目前已在IEEE Transactions on Artificial Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE/CAA Journal of Automatica Sinica, Elsevier Information Sciences, Elsevier Energy and Buildings等期刊和会议论文十余篇，是多个知名期刊的审稿人。

Zhang Yiru received his Bachelor's, Master's, and Ph.D. degrees from Huazhong University of Science and Technology in China, Université Sorbonne in France, and the University of Rennes in France in 2013, 2015, and 2020 respectively. He is currently an Associate Professor at Université CY Paris-Cergy in France, and also an invited lecturer at École Spéciale de Mécanique et d'Électricité (ESME), where he is responsible for teaching computer-related courses in algorithms and big data. He is also a research associate at the ETIS Laboratory of the French National Centre for Scientific Research (CNRS). His research interests include incomplete information reasoning based on belief function theory, decision theory, and time-series data mining algorithms. He has published over ten papers in journals and conferences such as IEEE Transactions on Artificial Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE/CAA Journal of Automatica Sinica, Elsevier Information Sciences, and Elsevier Energy and Buildings, and serves as a reviewer for several prestigious journals.

课程简介（中英文）：本课程将介绍主要的大数据框架和常用算法，这些框架通常用于管理和处理海量数据集。学生将学习Hadoop、Spark和其他流行的框架，并利用MapReduce等分布式计算算法和PCA等数据降维算法来分析大数据集。本课程还涵盖了数据科学、社交网络分析等相关主题，着重于实际应用和真实世界的用例。本课程包含基于Linux环境和python语言的实验练习模块以巩固学生对相关技术的理解与掌握。授课语言为英语。

This course will introduce major big data frameworks and common algorithms used for managing and processing massive datasets. Students will learn Hadoop, Spark, and other popular frameworks and utilize distributed computing algorithms such as MapReduce and data dimensionality reduction algorithms like PCA to analyze large datasets. The course also covers related topics in data science, social network analysis, with a focus on practical applications and real-world use cases. The course includes lab exercises based on Linux environment and Python language to reinforce students' understanding and mastery of relevant technologies. The course will be taught in English.