國立中山大學 106學年度第1學期 課程教學大綱

National Sun Yat-sen University 106Academic year1st Semester Course syllabus

中文名稱
Course name(Chinese)

巨量資料分析導論

課號
Course Code

MIS572

英文名稱
Course name(English)

INTRODUCTION TO BIG DATA ANALYTICS

課程類別
Type of the course

講授類

必選修
Required/Selected

選修

系所
Dept./faculty

資訊管理學系碩士班

授課教師
Instructor

康藝晃    

學分
Credit

3

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

         本課程教學大綱已提供完整英文資訊(本選項僅供統計使用,未提供完整英文資訊者,得免勾記)【Provide information of course syllabus in English.(This is for statistical use only. For those who do not provide information of course syllabus in English, do not check this field.)】

Students will be introduced to techniques and theories from many different fields, including probability & statistics, information theory, scientific computing, data engineering, database management, algorithm design, and statistical learning.
Prerequisite:
Basic understanding of relational databases, SQL, data structures, probability & statistics, college-level calculus, and matrix operations are required. Familiar with at least one high-level programming language. Scientific programming language, such as R, Matlab, Python, Scala, Julia, and SAS are preferred.







課程目標 Objectives

         This course is designed for students who need hands-on training of data analytics and general understanding of "Data Science". We will begin with introduction to the rise and evolution of the Data Science. Its applications in various fields, such as business, healthcare, insurance, and finance, will also be discussed. This course covers the basic concepts of Big Data Analytics, data management, SQL, NoSQL databases, Split-Apply-Combine strategy, MapReduce design patterns, Statistical Learning, In-memory Computing, and functional programming. Students will be introduced to how to use R programming language and its packages to solve real-world data problems, such as big data refinement, predictive modeling, recommendation engine design, and text analytics.

授課方式 Teaching methods

         1. Lecture
2. In-class quiz











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.In-class quiz30%
2.Homework30%
3.Project Proposal20%
4.Project Presentation20%

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

        
* N. Matloff, The Art of R Programming: A Tour of Statistical Software Design, 1st edition. San Francisco: No Starch Press, 2011.
* Kabacoff, Robert. R in Action. Manning Publications Co., 2011
* C. O'Neil and R. Schutt, Doing Data Science: Straight Talk from the Frontline, 1st edition. Beijing ; Sebastopol: O'Reilly Media, 2013.
* J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of Massive Datasets, 2 edition. Cambridge: Cambridge University Press, 2014.
(Available free online: http://www.mmds.org )
* G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: with Applications in R, 1st ed. 2013. Corr. 4th printing 2014 edition. New York: Springer, 2013.
(Available free online: http://www-bcf.usc.edu/~gareth/ISL/)
* EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. John Wiley & Sons, 2015.

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
12017/09/18~2017/09/24Course Overview
22017/09/25~2017/10/01Data Management and Analysis using R – I
32017/10/02~2017/10/08Data Management and Analysis using R – II
42017/10/09~2017/10/15Holiday Break (No Class)
52017/10/16~2017/10/22Data Management and Analysis using R – III
62017/10/23~2017/10/29Statistics and Probability Review - I
72017/10/30~2017/11/05Statistics and Probability Review - II
82017/11/06~2017/11/12Statistics and Probability Review - III
92017/11/13~2017/11/19Distributed File Systems & MapReduce Design Patterns - I
102017/11/20~2017/11/26Project Proposal Defense
112017/11/27~2017/12/03Distributed File Systems & MapReduce Design Patterns - II
122017/12/04~2017/12/10Introduction to Statistical Learning – I
132017/12/11~2017/12/17Introduction to Statistical Learning – II
142017/12/18~2017/12/24Introduction to Statistical Learning – III
152017/12/25~2017/12/31Introduction to Fast Scalable Data Analytics
162018/01/01~2018/01/07Holiday Break (No Class)
172018/01/08~2018/01/14Term Project Presentation - I
182018/01/15~2018/01/21Term Project Presentation - II

課業討論時間 Office hours

         時段1:
時間:星期二10AM-12PM
地點:CM 4083
時段2:
時間:星期三10AM-12PM
地點:CM 4083

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
本課程欲培養之能力與素養 This course enables students to achieve...紙筆考試或測驗 Test課堂討論︵含個案討論︶ Group discussion (case analysis)個人書面報告、作業、作品、實驗 Indivisual paper report/ assignment/ work or experiment群組書面報告、作業、作品、實驗 Group paper report/ assignment/ work or experiment個人口頭報告 Indivisual oral presentation群組口頭報告 Group oral presentation課程規劃之校外參訪及實習 Off-campus visit and intership證照/檢定 License參與課程規劃之校內外活動及競賽 Participate in off-campus/ on-campus activities and competitions課外閱讀 Outside reading
※系所所學生專業能力
1.具備資訊倫理的能力V          
2.具備溝通能力V          
3.具備解決資訊管理問題之能力V          
4.具備資訊管理之專業知識V          
5.具備國際觀           
※全校學生基本素養與核心能力
1.表達與溝通能力。           
2.探究與批判思考能力。           
3.終身學習能力。           
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。           
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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