Course Syllabus

Course Information：

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

Spring Semester
Year 2025 340504 Data Science 1 3.0 3 ★ Hong,Tzu-Yen
Shu-Kai Fan 4IEM3A
4IEM3B 37 0 工管三甲乙合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	340504	Data Science	1	3.0	3	★	Hong,Tzu-Yen Shu-Kai Fan	4IEM3A 4IEM3B	37	0	工管三甲乙合開

Course Plan：

Instructor Hong,Tzu-Yen　Office Hours

Email georgehong618@gmail.com

Last Updated 2024-12-19 16:58:22

Course Objective 在電子化與網路化之經營環境下，生產製造與服務過程中，都會蒐集其中自動產生或人工輸入的資料。本課程學習在資訊平台上，以統計與分析方法以處理大量資料混雜時的管理決策問題，以萃取出有價值的資訊或內隱的知識，做為決策支援與管理之依據。除介紹問題基本特性，說明資料科學工具與方法外，並利用作業、個案研究、案例資料，以訓練同學資料分析的能力與解決問題的經驗。 Amount of data are collected automatically during manufacturing production and service process. This course aims to introduce how to apply big data analytic and statistical methods for decision making with amount of data and then extracting useful rules or patterns for decision support and management. In addition to lecturing, this course also training students how to analyze the data and learn problem solving techniques through homework, term project and case studies.

Course Schedule 1 Introduction to data science 2 data preprocessing 3 Association rule 4 Classification: Decision tree models I 5 Classification: Decision tree models II 6 Paper Presentation 7 National Holiday 8 Clustering Methods I 9 Clustering Methods II 10 Holiday 11 Project Presentation I 12 Project Presentation II

Evaluation and grading policy Homework and Classwork 40% Paper Presentation 20% Project 30% Attendance 10%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.

The access to curricular consultation email to tyhong@ntut.edu.tw

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG17：夥伴關係（Partnerships for the Goals）

Note
　

Instructor	Hong,Tzu-Yen　Office Hours
Email	georgehong618@gmail.com
Last Updated	2024-12-19 16:58:22
Course Objective	在電子化與網路化之經營環境下，生產製造與服務過程中，都會蒐集其中自動產生或人工輸入的資料。本課程學習在資訊平台上，以統計與分析方法以處理大量資料混雜時的管理決策問題，以萃取出有價值的資訊或內隱的知識，做為決策支援與管理之依據。除介紹問題基本特性，說明資料科學工具與方法外，並利用作業、個案研究、案例資料，以訓練同學資料分析的能力與解決問題的經驗。 Amount of data are collected automatically during manufacturing production and service process. This course aims to introduce how to apply big data analytic and statistical methods for decision making with amount of data and then extracting useful rules or patterns for decision support and management. In addition to lecturing, this course also training students how to analyze the data and learn problem solving techniques through homework, term project and case studies.
Course Schedule	1 Introduction to data science 2 data preprocessing 3 Association rule 4 Classification: Decision tree models I 5 Classification: Decision tree models II 6 Paper Presentation 7 National Holiday 8 Clustering Methods I 9 Clustering Methods II 10 Holiday 11 Project Presentation I 12 Project Presentation II
Evaluation and grading policy	Homework and Classwork 40% Paper Presentation 20% Project 30% Attendance 10%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
The access to curricular consultation	email to tyhong@ntut.edu.tw
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG17：夥伴關係（Partnerships for the Goals）
Note