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) |
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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 | ||||||||||||||||||||||||||||||||||||||||
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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) |
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●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. |
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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. |
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四、課程內容 Course Description | ||||||||||||||||||||||||||||||||||||||||
●整體敘述 Overall Description |
●分週敘述 Weekly Schedule
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五、考評及成績核算方式 Grading | ||||||||||||||||||||||||
本科目 ☑同意/☐不同意 期末退修 | ||||||||||||||||||||||||
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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 ..... |
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●聯絡方式 Contact Info
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七、教學助理聯絡方式 TA’s Contact Info | |||||
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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 |
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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 | |||||