教學大綱與進度

課程基本資料：

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-1	353111	大數據分析	1	3.0	3	★	王正豪 鄭麗珍	管理外國學生專班 資財所	18	0	資財所和IMBA合開

教學大綱與進度：

教師姓名	王正豪　教師諮商時間(Office Hours)
Email	jhwang@ntut.edu.tw
最後更新時間	2025-08-06 16:58:47
課程大綱	This course introduces the methods and applications of big data analytics. The topics include big data management, data mining techniques, big data platforms, and project implementation and presentation. Students are expected to learn the concepts of big data analytics techniques, and their applications and future trends in different case studies.
課程進度	Part I: Data mining concepts (6 wks) 1. Introduction to big data concepts & data preprocessing (1 wk) 2. Frequent pattern mining (2 wks) 3. Data classification (2 wks) 4. Data clustering (1 wk) Week #8: Midterm exam (1 wk) Note: I'm responsible for the first part in 6 weeks (Week 2-7).
評量方式與標準	For Part I: - Homework assignments: ~60% - Midterm exam: ~40% The percentage of your score in Part I will be around 1/3 of the total score. It's roughly related to the number of weeks, and will be confirmed with Prof. Cheng.
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 - Textbook: Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 4th edition, Morgan Kaufmann, 2022. - References: 1. Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010. 2. Holden Karau, andy Konwinski, Patrick Wendell, and Matei Zaharia, Learning Spark: Lightning-fast data analysis, O'Reilly, 2015. 3. Recent academic papers, tutorials, and slides.
課程諮詢管道	The teacher can be reached via e-mail or messages in Microsoft Teams. The TA will help you join the course group in Facebook where you can also ask questions about the homeworks and exams.
課程對應SDGs指標	無（None）
課程是否導入AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
備註	All course sessions will be recorded whenever possible, and put on Microsoft Teams and iSchool+, along with all course materials, homework announcements and submissions. Latest news available soon at: https://chriswjh.github.io/BDA_IMBA/