大学院
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最終更新日：2026年4月20日
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最終更新日：2026年4月20日
授業計画や教室は変更となる可能性があるため、必ずUTASで最新の情報を確認して下さい。
UTASにアクセスできない方は、担当教員または部局教務へお問い合わせ下さい。
Data Science for Public Policy
Data Science for Public Policy
THIS COURSE WELCOMES STUDENTS FROM ALL BACKGROUNDS INCLUDING THOSE WHO HAVE NEVER CODED OR USED STATISTICAL MODELS
THIS COURSE IS ABOUT EDUCATING STUDENTS IN AI/LLM/AGENT USE FOR RESEARCH PURPOSES
Research Design & Methods:
Design reproducible research workflows using AI/Agents
Leverage public datasets to analyze policy problems
Create visualizations to communicate policy findings
Communicate findings clearly to broad audience
Technical Skills:
Understand AI/Agents in research workflows
Download, clean and analyze policy-relevant data
Develop scalable and replicable data infrastructure
Work collaboratively using professional data science tools (GitHub, Overleaf, Google Colab)
THIS COURSE IS ABOUT EDUCATING STUDENTS IN AI/LLM/AGENT USE FOR RESEARCH PURPOSES
Research Design & Methods:
Design reproducible research workflows using AI/Agents
Leverage public datasets to analyze policy problems
Create visualizations to communicate policy findings
Communicate findings clearly to broad audience
Technical Skills:
Understand AI/Agents in research workflows
Download, clean and analyze policy-relevant data
Develop scalable and replicable data infrastructure
Work collaboratively using professional data science tools (GitHub, Overleaf, Google Colab)
時間割／共通科目コード
コース名
教員
学期
時限
291324-12
GEC-EC6831L3
Data Science for Public Policy
BAIRD Cory
水曜2限
マイリストに追加
マイリストから削除
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
不可
開講所属
経済学研究科
授業計画
Module 1: How to Run Statistical Software (3 weeks)
Week 1: How to use AI Agents
Week 2: Downloading Data
Week 3: Cleaning Data
Module 2: Visualization (3 weeks)
Week 4: Basic visualization (lines, bars)
Week 5: Interactive visualization and mapping
Module 3: Text Analysis (3 weeks)
Week 7: TBA
Week 8: TBA
Week 9: TBA
Module 4: Modeling (4 weeks)
Week 10: TBA
Week 11: TBA
Week 12: TBA
Week 13: TBA
授業の方法
Students will watch instructor-made YouTube tutorials covering coding demonstrations and lecture content before class meetings, enabling more time for hands-on practice during our sessions
All course materials and code will be hosted on GitHub, giving students experience with professional version control tools while ensuring easy access to content
Class time emphasizes active learning through guided exercises, peer programming, and real-world policy data analysis challenges
成績評価方法
Group Assignments: 60%
Assignment 1 (20%)
Assignment 2 (20%)
Assignment 3 (20%)
Group Presentation: 40%
Students will analyze and present public data from sources like World Bank, IMF, or BIS, conducting either basic visualization (beginners) or statistical modeling (advanced students)
履修上の注意
This course welcomes students of all programming backgrounds, from complete beginners to those with coding experience. The structured progression from basic concepts to advanced applications ensures that everyone can succeed. The instructor has extensive experience teaching programming to diverse audiences, including many students who have never written a line of code. Through carefully designed tutorials, hands-on exercises, and personalized support, students from all backgrounds will develop the technical skills needed to apply data science to policy problems.
