查詢教學大綱與進度

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

112-1 322390 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
資訊安全學位學程
創新資安學位學程
管理博所 20 0 資財四甲乙所、IMBA、電資資安、創新資安和管理博班合

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
112-1	322390	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資訊安全學位學程創新資安學位學程管理博所	20	0	資財四甲乙所、IMBA、電資資安、創新資安和管理博班合

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 2023-05-29 17:30:57

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 (1 wk) 3. Data classification (2 wks) 4. Data clustering (1 wk) 5. Quiz (1 wk)

評量方式與標準 Homework assignments: ~60% Quiz: ~40%

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

備註
All course sessions will be recorded 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/

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	2023-05-29 17:30:57
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 (1 wk) 3. Data classification (2 wks) 4. Data clustering (1 wk) 5. Quiz (1 wk)
評量方式與標準	Homework assignments: ~60% Quiz: ~40%
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
備註	All course sessions will be recorded 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/