國立中山大學 105學年度第1學期 課程教學大綱

National Sun Yat-sen University 105Academic year1st Semester Course syllabus

中文名稱
Course name(Chinese)

 大數據探索

課號
Course Code

 GEAE2419

英文名稱
Course name(English)

 BIG DATA EXPLORER

課程類別
Type of the course

 講授類

必選修
Required/Selected

必修

系所
Dept./faculty

 博雅向度四＜科技與社會＞

授課教師
Instructor

 郭美惠    康藝晃    羅夢娜    

學分
Credit

 2

因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，授課方式調整如下：
　　　　　　　　 尚未建立傳染性肺炎(武漢肺炎)授課方式調整

因應嚴重特殊傳染性肺炎(武漢肺炎)，倘若後續需實施遠距授課，評分方式調整如下：

　　　　　　　　 尚未建立傳染性肺炎(武漢肺炎)課程評分方式﹝評分標準及比例﹞

課程大綱 Course syllabus
　　　　　　　　 (1) Data mining
(2) Finance big data challenges
(3) Statistical methods and computing for big data
(4) Matrix operation in Google Search Engine
(5) Machine learning
(6) Big Data analysis : Application in Industry
(7) Data Analytics for IOT Application
(8) Introduction to Big Data Analytics- -using Hadoop, Spark, and H2O
(9) Text Mining and Its Applications
(10) Cloud computing and big data
(11) Big Data meets HPC (high performance computing)

課程目標 Objectives
　　　　　　　　 (限100字以內)
This course is for those who are new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their future study or career.

授課方式 Teaching methods
　　　　　　　　 課堂講授

評分方式﹝評分標準及比例﹞Evaluation (Criteria and ratio)等第制單科成績對照表 letter grading reference

　　　　　　　　
1.群組口頭報告(1)：20%
2.書面報告(1)：20%
3.群組口頭報告(2)：20%
4.書面報告(2)：20%
5.平時成績：20%

參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念，不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use

　　　　　　　　 自編講義

彈性暨自主學習規劃 Alternative learning periods
本門課程是否有規劃實施學生彈性或自主學習內容（每1學分2小時）
Is any alternative learning periods planned for this course (with each credit corresponding to two hours of activity)?
否：教師需於「每週課程內容及預計進度」填寫18週課程進度（每1學分18小時之正課內容）。
No：The instructor will include an 18-week course plan in the weekly scheduled progress (each credit corresponds to 18 hours of instruction)
是：教師需於「每週課程內容及預計進度」填寫16週課程內容（每1學分16小時之正課內容），並於下列欄位填寫每1學分2小時學生彈性或自主學習內容。
    Yes：The instructor will include a 16-week course plan in the weekly scheduled progress (each credit corresponds to 16 hours of instruction);the details of the planned alternative learning periods are provided below (each credit corresponds to two hours of activity).

學生彈性或自主學習活動
Alternative learning periods		勾選或填寫規劃內容
Place a check in the appropriate box or provide details		時數
Number of hours
學生分組實作及討論
Group work and discussion
參與課程相關作業、作品、實驗
Participation in course-related assignments, work, or experiments
參與校內外活動（研習營、工作坊、參訪）或競賽
Participation in on- or off-campus activities (e.g., seminars, workshops, and visits) or competitions
課外閱讀
Extracurricular reading
線上數位教材學習
Learning with online digital learning materials
其他（請填寫規劃內容）
Other (please provide details)

每週課程內容及預計進度 Weekly scheduled progress
　　　　　　　　
週次日期授課內容及主題
12016/09/12~2016/09/18Mining big data
22016/09/19~2016/09/25Finance big data challenges
32016/09/26~2016/10/02Statistical methods and computing for big data
42016/10/03~2016/10/09Matrix operation in Google Search Engine
52016/10/10~2016/10/16Matrix operation in Google Search Engine
62016/10/17~2016/10/23Machine learning
72016/10/24~2016/10/30Applications of big data analysis in high-tech Industries
82016/10/31~2016/11/06The rise of big data on cloud computing
92016/11/07~2016/11/13(期中考週) 群組口頭報告及繳交書面報告
102016/11/14~2016/11/20Introduction to Big Data Analytics- -using Hadoop, Spark, and H2O
112016/11/21~2016/11/27Introduction to Big Data Analytics- -using Hadoop, Spark, and H2O
122016/11/28~2016/12/04Introduction to Big Data Analytics- -using Hadoop, Spark, and H2O
132016/12/05~2016/12/11Text Mining and Its Applications
142016/12/12~2016/12/18Text Mining and Its Applications
152016/12/19~2016/12/25Data Analytics for IOT Applications
162016/12/26~2017/01/01Big Data meets HPC (high performance computing)
172017/01/02~2017/01/08Big Data meets HPC (high performance computing)
182017/01/09~2017/01/15(期末考週) 群組口頭報告及繳交書面報告

課業討論時間 Office hours
　　　　　　　　 時段1:
時間：星期一14:10-16:00
地點：理4004
時段2：
時間：星期四15:10-17:00
地點：理4004

系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilitics of the dcpartment and the university
　　　　　　　　
系所學生專業能力/全校學生基本素養與核心能力課堂活動與評量方式
本課程預培養之能力與素養紙筆考試或測驗課堂討論︵含個案討論︶個人書面報告、作業、作品、實驗群組書面報告、作業、作品、實驗個人口頭報告群組口頭報告課程規劃之校外參訪及實習證照/檢定參與課程規劃之校內外活動及競賽課外閱讀
※全校學生基本素養與核心能力
1.表達與溝通能力。V   V V    
2.探究與批判思考能力。V V        
3.終身學習能力。V V        
4.倫理與社會責任。           
5.美感品味。           
6.創造力。           
7.全球視野。           
8.合作與領導能力。V   V V    
9.山海胸襟與自然情懷。           

本課程與SDGs相關項目：The course relates to SDGs items:

　　　　　　　　 尚未建立SDGS資料

本課程校外實習資訊: This course is relevant to internship:

　　　　　　　　 本課程無註記包含校外實習

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