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
111-1	309763	大數據分析	1	3.0	3	★	白敦文 黃柏鈞	AI雙聯專班	32	0	碩二要修，9/17,9/24,10/22,10/29,11/6,12/4

教學大綱與進度：

教師姓名	黃柏鈞　教師諮商時間(Office Hours)
Email	pchuang.19840320@gmail.com
最後更新時間	2022-05-25 00:27:23
課程大綱	本課程介紹大數據在各個領域決策制定的應用，課程內容包括大數據分析與應用、大數據管理、大數據分法與策略、資料探勘介紹、資料探勘方法、大數據平台、大數據行銷實務、大數據個案分析、期末專題製作與報告。學生可以經由多個案例學習大數據的應用實務、目前技術與未來發展方向。 This course explores the rapidly developing fields of big data for the efficiency of decision making in various fields. The topics are about basic concepts of data manipulations, analytic, and communications which are including: introduction to big data; basic knowledge of data mining; special topics of big data in business Analytics, and data analytic project. The course will help students careful consideration of what is desired from big data, how big data analytic to add value to business, etc. Students can gain a strong data science knowledge set and techniques via in-depth case studies, most current readings and experiences, and most current and future technology exploration. 線上上課連結：https://meet.google.com/eza-uqin-nam 11/27投影片下載：https://drive.google.com/file/d/16YnwJOs_dBFuJqr4Ad0KraS9xK2NddK4/view?usp=sharing
課程進度	Unit 1: Data analytics and business intelligence (lecture) Unit 2: Data warehousing (lecture) Unit 3: Data mining (lecture) Unit 4: Data visualization (lecture + lab) Unit 5: Decision tree (lecture + lab)Unit 6: Logistic regression (lecture + lab) Unit 7: Neural network (lecture + lab) Unit 8: Clustering analysis (lecture + lab) Unit 9: Association rules (lecture + lab) Unit 10: Text mining techniques (lecture + lab) Unit 11: Naïve Bayes classifiers (lecture + lab) Unit 12: Support vector machine (lecture + lab)
評量方式與標準	(Tentative) In-class participation: 40% Final demonstration/presentation: 60%
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 (Optional reference book) Pang-Ning Tan et al. Introduction to Data Mining, 2nd Edition.
課程諮詢管道	綜科309-1
備註	線上Google Meet混成教學