Course Syllabus

Course Information：

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

Fall Semester
Year 2026 366626 Data Mining and Big Data Analytics 1 3.0 3 ★ CHEN,LONG-SHENG GIEM 45 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366626	Data Mining and Big Data Analytics	1	3.0	3	★	CHEN,LONG-SHENG	GIEM	45	0

Course Plan：

Instructor CHEN,LONG-SHENG　Office Hours

Email lschen@ntut.edu.tw

Last Updated 2026-06-10 11:49:21

Course Objective 本課程旨在培養學生運用資料分析技術解決實務問題之能力。課程內容涵蓋資料型態與資料前處理，包括資料蒐集、清理、整合、轉換與特徵工程等資料準備技術；並介紹常用之資料探勘方法，如分類（Classification）、分群（Clustering）、關聯分析與預測模型建構。課程亦結合大數據分析概念與實務應用，培養學生對大量、多樣與高速資料之分析能力。此外，課程將探討自然語言處理（NLP）與文字探勘技術，包括斷詞、詞頻分析、情感分析、主題模型與文本分類等方法，應用於社群媒體、評論資料及文件分析。透過案例實作與分析工具操作，提升學生在人工智慧與資料科學領域之實務應用與研究能力。 This course aims to develop students’ abilities to apply data analysis techniques to solve practical problems. The course covers data types and data preprocessing, including data collection, cleaning, integration, transformation,and feature engineering techniques for data preparation. It also introduces commonly used data mining methods, such as classification, clustering,association analysis, and predictive model construction. In addition, the course integrates big data analytics concepts with practical applications to cultivate students’ capabilities in analyzing large-scale, diverse, and high-velocity data.Furthermore, the course explores Natural Language Processing (NLP) and text mining techniques, including word segmentation, term frequency analysis, sentiment analysis, topic modeling, and text classification, with applications in social media, review data, and document analysis. Through case studies and hands-on analytical tool practices, students will enhance their practical

Course Schedule Week 01 (09/07) : 資料探勘與大數據分析導論 Week 02 (09/14) : 資料與資料準備 Week 03 (09/21) : 關聯規則　 Week 04 (09/28) : 教師節放假　 Week 05 (10/05) : 分群 Week 06 (10/12) : 分群 Week 07 (10/19) : 分類 Week 08 (10/26) : 光復節補假 Week 09 (11/02) : 論文報告 Week 10 (11/09) : 校慶補假 Week 11 (11/16) : 分類 Week 12 (11/23) : 異常值偵測 Week 13 (11/30) : 自然語言處理 Week 14 (12/07) : 文字探勘 Week 15 (12/14) : 文字探勘實作 Week 16 (12/21) : 期末實作個案報告 Week 17 (12/28) : 期末實作個案報告 Week 18 (01/04) : 期末實作個案報告

Evaluation and grading policy 課堂參與&平時作業 30%、論文報告30%、個案實作40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
1.簡富、許嘉裕編著，大數據分析與資料挖礦，前程文化出版，2018。 2.Han : Data Mining: Concepts and Techniques 4/e 3.自編教材

The access to curricular consultation 1.Office Hours，採預約制
2.線上詢問:Line或email

Expanding teaching and resources ● 無 (None)

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）

Note
　

Instructor	CHEN,LONG-SHENG　Office Hours
Email	lschen@ntut.edu.tw
Last Updated	2026-06-10 11:49:21
Course Objective	本課程旨在培養學生運用資料分析技術解決實務問題之能力。課程內容涵蓋資料型態與資料前處理，包括資料蒐集、清理、整合、轉換與特徵工程等資料準備技術；並介紹常用之資料探勘方法，如分類（Classification）、分群（Clustering）、關聯分析與預測模型建構。課程亦結合大數據分析概念與實務應用，培養學生對大量、多樣與高速資料之分析能力。此外，課程將探討自然語言處理（NLP）與文字探勘技術，包括斷詞、詞頻分析、情感分析、主題模型與文本分類等方法，應用於社群媒體、評論資料及文件分析。透過案例實作與分析工具操作，提升學生在人工智慧與資料科學領域之實務應用與研究能力。 This course aims to develop students’ abilities to apply data analysis techniques to solve practical problems. The course covers data types and data preprocessing, including data collection, cleaning, integration, transformation,and feature engineering techniques for data preparation. It also introduces commonly used data mining methods, such as classification, clustering,association analysis, and predictive model construction. In addition, the course integrates big data analytics concepts with practical applications to cultivate students’ capabilities in analyzing large-scale, diverse, and high-velocity data.Furthermore, the course explores Natural Language Processing (NLP) and text mining techniques, including word segmentation, term frequency analysis, sentiment analysis, topic modeling, and text classification, with applications in social media, review data, and document analysis. Through case studies and hands-on analytical tool practices, students will enhance their practical
Course Schedule	Week 01 (09/07) : 資料探勘與大數據分析導論 Week 02 (09/14) : 資料與資料準備 Week 03 (09/21) : 關聯規則　 Week 04 (09/28) : 教師節放假　 Week 05 (10/05) : 分群 Week 06 (10/12) : 分群 Week 07 (10/19) : 分類 Week 08 (10/26) : 光復節補假 Week 09 (11/02) : 論文報告 Week 10 (11/09) : 校慶補假 Week 11 (11/16) : 分類 Week 12 (11/23) : 異常值偵測 Week 13 (11/30) : 自然語言處理 Week 14 (12/07) : 文字探勘 Week 15 (12/14) : 文字探勘實作 Week 16 (12/21) : 期末實作個案報告 Week 17 (12/28) : 期末實作個案報告 Week 18 (01/04) : 期末實作個案報告
Evaluation and grading policy	課堂參與&平時作業 30%、論文報告30%、個案實作40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 1.簡富、許嘉裕編著，大數據分析與資料挖礦，前程文化出版，2018。 2.Han : Data Mining: Concepts and Techniques 4/e 3.自編教材
The access to curricular consultation	1.Office Hours，採預約制 2.線上詢問:Line或email
Expanding teaching and resources	● 無 (None)
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
Note