Data Science

1年

2.00単位

セメスタ指定なし

小松　悟朗

English

紀尾井町キャンパス

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.

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

01 Introduction to R (1) (QSS Ch.1)

Read an assignment

Solve and submit homework quizzes

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

Read an assignment

Solve and submit homework quizzes

03 Experimental Studies (QSS 2.1-2.2)

Read an assignment

Solve and submit homework quizzes

04 Observational Studies (QSS 2.3-2.6)

Read an assignment

Solve and submit homework quizzes

05 Measurement (QSS Ch.3)

Read an assignment

Solve and submit homework quizzes

06 Prediction (QSS Ch.4)

Read an assignment

Solve and submit homework quizzes

07 Simple Regression Analysis (1) (Wooldridge, Ch.2)

Read an assignment

Solve and submit homework quizzes

08 Simple Regression Analysis (2) (Wooldridge, Ch.3)

Read an assignment

Solve and submit homework quizzes

09 Multiple Regression Analysis (1) (Wooldridge, Ch.4,5,6)

Read an assignment

Solve and submit homework quizzes

10 Multiple Regression Analysis (2) (Wooldridge, Ch.7,8,9)

Read an assignment

Solve and submit homework quizzes

11 Panel Data, Difference-in-Differences (Wooldridge, Ch.14,15)

Read an assignment

Solve and submit homework quizzes

12 Instrumental Variables, RMarkdown (Wooldridge, Ch.13)

Read an assignment

Solve and submit homework quizzes

13 Final-Project Presentation

Read an assignment

Solve and submit homework quizzes

Problem Sets: 50%
Final-Project Presentation: 50%

To be provided when necessary/requested

To be provided

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“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

4501 Kioicho Campus

TBA