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.
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
1
2017/09/18~2017/09/24
Course Overview
2
2017/09/25~2017/10/01
Data Management and Analysis using R – I
3
2017/10/02~2017/10/08
Data Management and Analysis using R – II
4
2017/10/09~2017/10/15
Holiday Break (No Class)
5
2017/10/16~2017/10/22
Data Management and Analysis using R – III
6
2017/10/23~2017/10/29
Statistics and Probability Review - I
7
2017/10/30~2017/11/05
Statistics and Probability Review - II
8
2017/11/06~2017/11/12
Statistics and Probability Review - III
9
2017/11/13~2017/11/19
Distributed File Systems & MapReduce Design Patterns - I
10
2017/11/20~2017/11/26
Project Proposal Defense
11
2017/11/27~2017/12/03
Distributed File Systems & MapReduce Design Patterns - II