専門英語（４０）

Week 1. (Apr 6) Guidance Week 2. (Apr. 20) Introduction to R - Installing R and R Studio - Calculation, creation/manipulation of objects, and data loading using R Week 3. (Apr. 27) Working with datasets available online - Examples of online datasets - Web-scraping using R - Using APIs *Assignment 1: Due May 7* Week 4. (May 11) Data visualization - Basic charts - Using ggplot Week 5. (May 18) Geographic Data and TIme-Series Data *Assignment 2: Due May 31* Week 6. (May 25) Basic statistics and probability distributions (1) - Random variables - Probability distributions - Discussing the final project Week 7. (Jun 1) Basic statistics and probability distributions (2) - Sample and Population - Law of Large Numbers and Central Limit Theorem - Hypothesis testing *Assignment 3: Due June 7* Week 8. (Jun 8) Regression (1) - Ordinary Least Squares Week 9. (Jun 15) Regression (2) - Omitted variable bias - Categorical variables: logit and probit regression Week 10. (Jun 22) Regression (3) - Time-series and other topics *Assignment 4: Due June 28* Week 11. (Jun 29) - Q&A and backup Weeks 12-13. (July 6 & 13) Presentation of final projects

Online lectures and in-class labs

Class participation: 20% Bi-weekly assignments: 40% Final project (presentation and final paper): 40%

Long, James D., and Paul Teetor. R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. 2nd Edition. O'Reilly Media, 2019. (The online version is available for free.) Gelman, Andrew, and Jennifer Hill. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, 2006.

Imai, Kosuke (2018). Quantitative social science: An introduction. Princeton University Press.

The focus of this course is on learning programing skills of R to do data management, visualization and analysis, so a mathematical background in probability and statistics is not a prerequisite, but students are strongly encouraged to complement this course with these fundamentals.