学部後期課程
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学内で開催されるオンライン授業の情報漏えい防止のため,2021年5月7日以降は授業カタログの更新を見合わせています

専門英語(40)

This course introduces students to basic skills for data management, analysis and visualization using R, a free and open source software. By the end of the course, students will be equipped with the foundation to conduct data analysis and effectively present their findings in the field of political science, as well as to critically assess scientific evidence shown in academic research, news reports, etc. that use statistical methods.
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時間割/共通科目コード
コース名
教員
学期
時限
08B0040
FAS-BA4A01S3
専門英語(40)
吉本 郁
S1 S2
火曜5限
マイリストに追加
マイリストから削除
教室
講義使用言語
英語
単位
2
実務経験のある教員による授業科目
NO
他学部履修
開講所属
教養学部
授業計画
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
授業の方法
Online lectures and in-class labs
成績評価方法
Class participation: 20% Bi-weekly assignments: 40% Final project (presentation and final paper): 40%
教科書
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.
参考書
Imai, Kosuke (2018). Quantitative social science: An introduction. Princeton University Press.
履修上の注意
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.