東吳大學教師授課計劃表

檔案產生時間:2022/12/5 上午 05:15:00
本表如有異動,於4小時內自動更新
一、課程基本資料 Course Information
科目名稱 Course Title:
(中文)新式資料分析方法-R程式之應用全英語授課
(英文)MODERN_DATA_ANALYSIS WITH R
開課學期 Semester:111學年度第1學期
開課班級 Class:經三A (合開:經三B 經三C)
授課教師 Instructor:米克里斯多 MICHALOPOULOS, CHRISTOS
科目代碼 Course Code:BEC37401 單全學期 Semester/Year:單 分組組別 Section:全英語授課
人數限制 Class Size:70 必選修別 Required/Elective:選 學分數 Credit(s):3
星期節次 Day/Session: 三34E  前次異動時間 Time Last Edited:111年06月12日12時56分
經濟學系基本能力指標 Basic Ability Index
編號
Code
指標名稱
Basic Ability Index
本科目對應之指標
Correspondent Index
達成該項基本能力之考評方式
Methods Of Evaluating This Ability
1具備經濟學核心知識
Core economic knowledge.
》出缺席狀況
》課堂討論與表現
》作業成績
》資料蒐集與分析
》外文閱讀
2具備經濟應用及政策分析能力
The ability to apply economic theories and conduct policy analysis.
》出缺席狀況
》課堂討論與表現
》報告(含個人或小組、口頭或書面、專題、訪問、觀察等形式)
》外文閱讀
》團隊參與
3具備邏輯思考能力
The ability of logical thinking.
》出缺席狀況
》課堂討論與表現
》資料蒐集與分析
4具備數理分析能力
The ability to perform mathematical analysis.
》出缺席狀況
》課堂討論與表現
》作業成績
》外文閱讀
5具備統計分析能力
The ability to perform statistical analysis.
》出缺席狀況
》課堂討論與表現
》外文閱讀
6具備金融與財務專業能力
Professional skills in money, banking and finance.
》資料蒐集與分析
7具備資料收集及表達能力
The ability to gather information and to make presentations.
》報告(含個人或小組、口頭或書面、專題、訪問、觀察等形式)
》團隊參與
8具備英文閱讀能力
The ability to read English proficiently.
》出缺席狀況
》課堂討論與表現
》報告(含個人或小組、口頭或書面、專題、訪問、觀察等形式)
》作業成績
》外文閱讀
》團隊參與
二、指定教科書及參考資料 Textbooks and Reference
(請修課同學遵守智慧財產權,不得非法影印)
●指定教科書 Required Texts
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani - An Introduction to Statistical Learning - with Applications in R (2021).pdf
●參考書資料暨網路資源 Reference Books and Online Resources
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani - An Introduction to Statistical Learning - with Applications in R (2021).pdf
三、教學目標 Objectives
This is an introduction to modern methods in data analysis using R. Students need to know many methods to analyze data that come from different sources not only economics. The class’ aim is to teach you how to apply a variety of methods to real data using R. After teaching R (plotting, writing functions) and basic statistical techniques (sampling distributions, bootstrap and permutation tests) we start off with an introduction to machine learning and especially classification of new data from old. Then, a number of regression techniques is introduced to deal with small and very large data sets (more parameters than data) together with data smoothing techniques (joining linear functions together). The important modern topic of causal inference is treated at a simple enough level with many practical examples. Important methods of uncovering causal relations like matching and instrumental variables are taught at an elementary level with many data examples. If time permits, basic time series topics are discussed.
After this class, the student will be ready to have answers to the following questions:
(1) How can we extract information from data without imposing assumptions on them?
(2) What can we do when we have more variables than observations?
(3) When can we say the relation between some variables is causal and not just a simple correlation.
All relevant concepts will be gradually introduced with many examples. This class can be taken together with Econometrics since they are complements. Lecture notes will be provided at the class so there is no need for a textbook. There will be no exam, only homework.
This is an introduction to modern methods in data analysis using R. Students need to know many methods to analyze data that come from different sources not only economics. The class’ aim is to teach you how to apply a variety of methods to real data using R. After teaching R (plotting, writing functions) and basic statistical techniques (sampling distributions, bootstrap and permutation tests) we start off with an introduction to machine learning and especially classification of new data from old. Then, a number of regression techniques is introduced to deal with small and very large data sets (more parameters than data) together with data smoothing techniques (joining linear functions together). The important modern topic of causal inference is treated at a simple enough level with many practical examples. Important methods of uncovering causal relations like matching and instrumental variables are taught at an elementary level with many data examples. If time permits, basic time series topics are discussed.
After this class, the student will be ready to have answers to the following questions:
(1) How can we extract information from data without imposing assumptions on them?
(2) What can we do when we have more variables than observations?
(3) When can we say the relation between some variables is causal and not just a simple correlation.
All relevant concepts will be gradually introduced with many examples. This class can be taken together with Econometrics since they are complements. Lecture notes will be provided at the class so there is no need for a textbook. There will be no exam, only homework.
四、課程內容 Course Description
整體敘述 Overall Description
●分週敘述 Weekly Schedule
週次 Wk 日期 Date 課程內容 Content 備註 Note

1

9/7 Statistical Learning and introduction to R.   

2

9/14 Statistical Learning and introduction to R.   

3

9/21 Linear Regression   

4

9/28 Linear Regression   

5

10/5 Classification methods   

6

10/12 Classification methods   

7

10/19 Generalized Linear Model   

8

10/26 Generalized Linear Model   

9

11/2 Regression smoothing techniques (Local linear regression, Kernel regression   

10

11/9 Resampling Methods i

11

11/16 Resampling Methods   

12

11/23 Linear Model Selection and Regularization   

13

11/30 Linear Model Selection and Regularization   

14

12/7 Linear Model Selection and Regularization   

15

12/14 Moving Beyond Linearity   

16

12/21 Moving Beyond Linearity   

17

12/28 Moving Beyond Linearity   

18

1/4 Project.   
五、考評及成績核算方式 Grading
本科目 ☑同意/☐不同意 期末退修
配分項目 Items 次數 Times 配分比率 Percentage 配分標準說明 Grading Description
出席1830% 
平時作業525% 
報告125% 
課堂討論 20% 
配分比率加總 100%  
六、授課教師課業輔導時間和聯絡方式 Office Hours And Contact Info
●課業輔導時間 Office Hour
.....
●聯絡方式 Contact Info
研究室地點 Office:3413 EMAIL:
聯絡電話 Tel: 其他 Others:
七、教學助理聯絡方式 TA’s Contact Info
教學助理姓名 Name 連絡電話 Tel EMAIL 其他 Others
八、建議先修課程 Suggested Prerequisite Course
None. All taught in class.
九、課程其他要求 Other Requirements
十、學校教材上網、數位學習平台及教師個人網址 University’s Web Portal And Teacher's Website
學校教材上網網址 University’s Teaching Material Portal:
東吳大學Moodle數位平台:http://isee.scu.edu.tw
學校數位學習平台 University’s Digital Learning Platform:
☐東吳大學Moodle數位平台:http://isee.scu.edu.tw
☐東吳大學Tronclass行動數位平台:https://tronclass.scu.edu.tw
教師個人網址 Teacher's Website:
其他 Others:
十一、計畫表公布後異動說明 Changes Made After Posting Syllabus