シラバス情報

授業科目名
Data Science
学年
1年
単位数
2.00単位
実務経験の有無
開講クォーター
セメスタ指定なし
担当教員
小松 悟朗
授業形態
授業で主に使用する言語
English
授業方法区分
開講キャンパス
紀尾井町キャンパス
授業の到達目標及びテーマ
This course provides advanced statistical methods for Causal Inference and Econometrics analysis.

Students will also have hands-on experiences on R programming languages, to apply those methodologies to real world data.

No programming skills are required. We also welcome those who are interested in programming and data scientists.

*Students must bring their own laptop computers for the in-class programming exercises.

*It is highly recommended that students have already taken credits in Statistics offered by GSIA.
*It is also strongly recommended to register for this class if you plan to use Regression Analysis for your master thesis.
授業の概要
Course Title: Data Science

Class Format: Lecture

Content:
The course consists of two parts. In the first part, students have hands-on experiences in R programming languages with the instructor. Students then apply those programming skills to Econometric analysis in the second part of the class.

Keyword:
Data Science, Econometrics, Experimental Studies, Randomized Controlled Trials (RCT), Observational Studies, Causal Inference, Regression, Programing, Data Visualization, R



Week 1—Introduction to R
01 Introduction to R (1) (QSS Ch.1)
02 Introduction to R (2) (QSS Ch.1 Exercises)

Week 2—Causality
03 Experimental Studies (QSS 2.1-2.2)
04 Observational Studies (QSS 2.3-2.6)

Week 3—Measurement, Prediction
05 Measurement (QSS Ch.3)
06 Prediction (QSS Ch.4)

Week 4—Simple Regression Analysis
07 Simple Regression Analysis (1) (Wooldridge, Ch.2)
08 Simple Regression Analysis (2) (Wooldridge, Ch.3)

Week 5—Multiple Regression Analysis
09 Multiple Regression Analysis (1) (Wooldridge, Ch.4,5,6)
10 Multiple Regression Analysis (2) (Wooldridge, Ch.7,8,9)

Week 6—Causal Inference and Program Evaluation
11 Panel Data, Difference-in-Differences (Wooldridge, Ch.14,15)
12 Instrumental Variables, RMarkdown (Wooldridge, Ch.13)

Week 7—Presentation
13 Final-Project Presentation

授業計画
1回
01 Introduction to R (1) (QSS Ch.1)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

2回
02 Introduction to R (2) (QSS Ch.1 Exercises)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

3回
03 Experimental Studies (QSS 2.1-2.2)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

4回
04 Observational Studies (QSS 2.3-2.6)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

5回
05 Measurement (QSS Ch.3)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

6回
06 Prediction (QSS Ch.4)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

7回目
07 Simple Regression Analysis (1) (Wooldridge, Ch.2)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

8回
08 Simple Regression Analysis (2) (Wooldridge, Ch.3)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

9回
09 Multiple Regression Analysis (1) (Wooldridge, Ch.4,5,6)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

10回
10 Multiple Regression Analysis (2) (Wooldridge, Ch.7,8,9)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

11回
11 Panel Data, Difference-in-Differences (Wooldridge, Ch.14,15)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

12回
12 Instrumental Variables, RMarkdown (Wooldridge, Ch.13)
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

13回
13 Final-Project Presentation
事前学習
Read an assignment
事後学習
Solve and submit homework quizzes

14回
事前学習
事後学習

15回
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事後学習

16回
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17回
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18回
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19回
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20回
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21回
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事後学習

22回
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事後学習

23回目
事前学習
事後学習

24回
事前学習
事後学習

25回
事前学習
事後学習

26回
事前学習
事後学習

試験及び成績評価
Problem Sets: 50%
Final-Project Presentation: 50%
課題(試験やレポート等)に対するフィードバック
To be provided when necessary/requested
講義で使用するテキスト(書名・著者・出版社・ISBN・備考)
To be provided
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参考文献・推薦図書
“QSS”
Quantitative Social Science: An Introduction
Kosuke Imai
Princeton University Press.
ISBN: 9780691175461
2017

“Wooldridge”
Introductory Econometrics: A Modern Approach (7th Edition)
Wooldridge
South-Western Pub.
ISBN: 9781337558860
2019
研究室
4501 Kioicho Campus
オフィスアワー
TBA
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