Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 356433 Big data analysis and machine learning 1 2.0 2 ★ Fu, Yuan Chih GTVE 5 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	356433	Big data analysis and machine learning	1	2.0	2	★	Fu, Yuan Chih	GTVE	5	0

Course Plan：

Instructor Fu, Yuan Chih　Office Hours

Email fuyc20@gmail.com

Last Updated 2025-12-19 15:21:37

Course Objective 本課程旨在提供社會科學領域背景的碩博士研究生熟悉並掌握機器學習的分析技術，並能將其運用在分析巨量的結構(數字)或非結構數據資料(文字、圖像、社群關係)。課程內容將以python作為數據分析的軟體，並介紹如何使用網絡爬蟲以及API服務擷取並整理所需要的目標數據，此外將介紹包括知識圖譜的繪製、肢體動作圖像辨識、自然語言處理的技術以及應用等。由於本課程需要大量的程式編寫作業，並須有一定的程式與統計基礎，學生須先修過教育統計學和校務研究，始可修習本課程。另曾修習多變量分析課程者尤佳。 This course is tailored for graduate students majoring in social sciences, equipping them with the skills to proficiently employ machine learning in their big data analysis endeavors. The applications of machine learning encompass both structured and unstructured data analysis. The course will include instruction on Python programming. Topics covered will encompass web scraping, data acquisition via API services, science mapping, motion detection, and natural language processing. This course places a high demand on both programming skills and a solid foundation in statistics. Prerequisite courses for enrollment in this course include educational statistics and institutional research. And taking multivariate analysis before this course is highly recommend although it is not requirement.

Course Schedule 第一週：學期課程介紹與程式環境設定第二週：認知與腦神經科學關係第三週：腦神經科學訊號收集與數據分析第四週：肢體動作與運動科學第五週：肢體動作訊號收集與數據分析第六週：專題實作：閱讀習慣或動作習慣的辨識第七週：地理空間資訊系統與全球圖層界接第八週：臺灣人口結構特徵圖繪製第九週：公共基礎設施可近性：以醫療院所為例第十週：代理人模型介紹第十一週：代理人模型與地理空間資訊系統整合分析第十二週：專題實作：理想醫療院所區位的選定第十三週：數位文本與自然語言處理第十四週：應用生成式AI標記文本資料第十五週：鄉土地方誌的深度解析第十六週：期末報告：優化高齡個人生活品質第十七週：期末報告：優化高齡社會資源配置第十八週：課程回顧 Week 1: Course Overview and Programming Environment Setup Week 2: The Relationship between Cognition and Neuroscience Week 3: Neuroscience Signal Acquisition and Data Analysis Week 4: Human Movement and Motor Science Week 5: Motion Signal Acquisition and Data Analysis Week 6: Project-Based Practice: Identifying Reading Habits or Movement Patterns Week 7: Geographic Information Systems (GIS) and Global Map Layer Integration Week 8: Mapping Taiwan’s Population Structure Week 9: Accessibility of Public Infrastructure: The Case of Healthcare Facilities Week 10: Introduction to Agent-Based Modeling Week 11: Integrated Analysis of Agent-Based Models and Geographic Information Systems Week 12: Project-Based Practice: Identifying Optimal Locations for Healthcare Facilities Week 13: Digital Texts and Natural Language Processing Week 14: Applying Generative AI to Text Data Annotation Week 15: In-Depth Analysis of Local Gazetteers and Community Histories Week 16: Final Project Presentation: Optimizing Quality of Life for Older Adults Week 17: Final Project Presentation: Optimizing the Allocation of Social Resources for an Aging Society Week 18: Course Review

Evaluation and grading policy Assessment and Grading Criteria Class Participation and Attendance: 30% Project-Based Assignments: 30% Final Project Report: 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Instructional Materials: Instructor-Developed Python Materials

The access to curricular consultation Teaching Assistant (TA) Lab Sessions:
Hands-on practice sessions led by teaching assistants will be offered on Tuesday and Thursday mornings.

The course corresponds to the SDGs SDG1：消除貧窮（No Poverty）
SDG4：優質教育（Quality Education）
SDG11：永續城市與社區（Sustainable Cities and Communities）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Fu, Yuan Chih　Office Hours
Email	fuyc20@gmail.com
Last Updated	2025-12-19 15:21:37
Course Objective	本課程旨在提供社會科學領域背景的碩博士研究生熟悉並掌握機器學習的分析技術，並能將其運用在分析巨量的結構(數字)或非結構數據資料(文字、圖像、社群關係)。課程內容將以python作為數據分析的軟體，並介紹如何使用網絡爬蟲以及API服務擷取並整理所需要的目標數據，此外將介紹包括知識圖譜的繪製、肢體動作圖像辨識、自然語言處理的技術以及應用等。由於本課程需要大量的程式編寫作業，並須有一定的程式與統計基礎，學生須先修過教育統計學和校務研究，始可修習本課程。另曾修習多變量分析課程者尤佳。 This course is tailored for graduate students majoring in social sciences, equipping them with the skills to proficiently employ machine learning in their big data analysis endeavors. The applications of machine learning encompass both structured and unstructured data analysis. The course will include instruction on Python programming. Topics covered will encompass web scraping, data acquisition via API services, science mapping, motion detection, and natural language processing. This course places a high demand on both programming skills and a solid foundation in statistics. Prerequisite courses for enrollment in this course include educational statistics and institutional research. And taking multivariate analysis before this course is highly recommend although it is not requirement.
Course Schedule	第一週：學期課程介紹與程式環境設定第二週：認知與腦神經科學關係第三週：腦神經科學訊號收集與數據分析第四週：肢體動作與運動科學第五週：肢體動作訊號收集與數據分析第六週：專題實作：閱讀習慣或動作習慣的辨識第七週：地理空間資訊系統與全球圖層界接第八週：臺灣人口結構特徵圖繪製第九週：公共基礎設施可近性：以醫療院所為例第十週：代理人模型介紹第十一週：代理人模型與地理空間資訊系統整合分析第十二週：專題實作：理想醫療院所區位的選定第十三週：數位文本與自然語言處理第十四週：應用生成式AI標記文本資料第十五週：鄉土地方誌的深度解析第十六週：期末報告：優化高齡個人生活品質第十七週：期末報告：優化高齡社會資源配置第十八週：課程回顧 Week 1: Course Overview and Programming Environment Setup Week 2: The Relationship between Cognition and Neuroscience Week 3: Neuroscience Signal Acquisition and Data Analysis Week 4: Human Movement and Motor Science Week 5: Motion Signal Acquisition and Data Analysis Week 6: Project-Based Practice: Identifying Reading Habits or Movement Patterns Week 7: Geographic Information Systems (GIS) and Global Map Layer Integration Week 8: Mapping Taiwan’s Population Structure Week 9: Accessibility of Public Infrastructure: The Case of Healthcare Facilities Week 10: Introduction to Agent-Based Modeling Week 11: Integrated Analysis of Agent-Based Models and Geographic Information Systems Week 12: Project-Based Practice: Identifying Optimal Locations for Healthcare Facilities Week 13: Digital Texts and Natural Language Processing Week 14: Applying Generative AI to Text Data Annotation Week 15: In-Depth Analysis of Local Gazetteers and Community Histories Week 16: Final Project Presentation: Optimizing Quality of Life for Older Adults Week 17: Final Project Presentation: Optimizing the Allocation of Social Resources for an Aging Society Week 18: Course Review
Evaluation and grading policy	Assessment and Grading Criteria Class Participation and Attendance: 30% Project-Based Assignments: 30% Final Project Report: 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Instructional Materials: Instructor-Developed Python Materials
The access to curricular consultation	Teaching Assistant (TA) Lab Sessions: Hands-on practice sessions led by teaching assistants will be offered on Tuesday and Thursday mornings.
The course corresponds to the SDGs	SDG1：消除貧窮（No Poverty） SDG4：優質教育（Quality Education） SDG11：永續城市與社區（Sustainable Cities and Communities）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note