Basic courses B: Evidence Development Methodologies Required Electives
| Title | 15. | Data Science for Public Policy Held by the Graduate School of: Public Policy Number: 5123038 |
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| Held by the Graduate School of: Public Policy / Doctoral Number: 5171023 |
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| Held by the Graduate School of: Public Policy / Doctoral Number: 5173105 |
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| Held by the Graduate School of: Economics Number: 291324-12 |
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| Instructor | BAIRD Cory | |
| Schedule | S1S2 / Mon. 2nd[10:25～12:10] |
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| Language | English | |
| Credit | 2 | |
| Room | Please check the venue (online / classroom location) by each course on UTAS or UTOL. | |
| Abstract | Important note: Prior coding or statistical modeling experience not required. Learn basic visualilzation and statistical modeling to cutting-edge techniques like LLMs (ChatGPT). This course provides rigorous training to create reproducible research in economics and public policy. Open to all skill levels. - Use Python to collect, clean, and analyze policy-relevant data. - Design and implement reproducible research workflows to effectively manage and utilize public data. - Apply statistical and machine learning methods to analyze policy problems. - Process and analyze text data using traditional NLP and modern LLMs (ChatGPT) to extract meaningful insights. - Develop visualization to communicate research findings effectively to both technical and non-technical audiences. - Collaborate effectively using professional data science tools like GitHub, Overleaf, and Google Colab. |