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 366648 Big Data Analytics 1 3.0 3 ★ Jenq-Haur Wang
Cheng, Li-Chen IMBA
GIFM 19 0 資財所和IMBA合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366648	Big Data Analytics	1	3.0	3	★	Jenq-Haur Wang Cheng, Li-Chen	IMBA GIFM	19	0	資財所和IMBA合開

Course Plan：

Instructor Cheng, Li-Chen　Office Hours

Email jessicacheng@mail.ntut.edu.tw

Last Updated 2026-06-04 13:52:58

Course Objective 本課程介紹大數據在各個領域決策制定的應用，課程內容包括大數據分析與應用、大數據管理、大數據分法與策略、資料探勘介紹、資料探勘方法、大數據平台、大數據行銷實務、大數據個案分析、期末專題製作與報告。學生可以經由多個案例學習大數據的應用實務、目前技術與未來發展方向。這門課要閱讀期刊論文且要熟悉 python This course explores the rapidly developing fields of big data for the efficiency of decision making in various fields. The topics are about basic concepts of data manipulations, analytic, and communications which are including: introduction to big data; basic knowledge of data mining; special topics of big data in business Analytics, and data analytic project. The course will help students careful consideration of what is desired from big data, how big data analytic to add value to business, etc. Students can gain a strong data science knowledge set and techniques via in-depth case studies, most current readings and experiences, and most current and future technology exploration.

Course Schedule Topic I: data preprocessing and DM tool 1. Be familiar of business analytics potential and dangers in today’s data rich-environment 2. Gain experience using key data mining methods of classification, prediction, data reduction and exploration 3. Know how to decide when to use which technique 4. Be able to implement major data mining techniques using software Topic II paper study project development

Evaluation and grading policy paper study and presentation and feedback：20％ Hand on lab 40% Final team project report and presentation: 40% This course also discusses the journal paper to enhance the students' theoretical base NOTE: This is an advanced course. Student also should familiar with python

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Data Mining for Business Analytics [Python Edition](link is external), John Wiley & Sons, 2019

The access to curricular consultation lijen.cheng@gmail.com
name class and question

Expanding teaching and resources ● 數位科技融入 (Digital Technology Integration)
運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。
Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.

The course corresponds to the SDGs SDG3：良好健康和福祉（Good Health and Well-being）
SDG4：優質教育（Quality Education）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）

Note
teams

Instructor	Cheng, Li-Chen　Office Hours
Email	jessicacheng@mail.ntut.edu.tw
Last Updated	2026-06-04 13:52:58
Course Objective	本課程介紹大數據在各個領域決策制定的應用，課程內容包括大數據分析與應用、大數據管理、大數據分法與策略、資料探勘介紹、資料探勘方法、大數據平台、大數據行銷實務、大數據個案分析、期末專題製作與報告。學生可以經由多個案例學習大數據的應用實務、目前技術與未來發展方向。這門課要閱讀期刊論文且要熟悉 python This course explores the rapidly developing fields of big data for the efficiency of decision making in various fields. The topics are about basic concepts of data manipulations, analytic, and communications which are including: introduction to big data; basic knowledge of data mining; special topics of big data in business Analytics, and data analytic project. The course will help students careful consideration of what is desired from big data, how big data analytic to add value to business, etc. Students can gain a strong data science knowledge set and techniques via in-depth case studies, most current readings and experiences, and most current and future technology exploration.
Course Schedule	Topic I: data preprocessing and DM tool 1. Be familiar of business analytics potential and dangers in today’s data rich-environment 2. Gain experience using key data mining methods of classification, prediction, data reduction and exploration 3. Know how to decide when to use which technique 4. Be able to implement major data mining techniques using software Topic II paper study project development
Evaluation and grading policy	paper study and presentation and feedback：20％ Hand on lab 40% Final team project report and presentation: 40% This course also discusses the journal paper to enhance the students' theoretical base NOTE: This is an advanced course. Student also should familiar with python
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Data Mining for Business Analytics [Python Edition](link is external), John Wiley & Sons, 2019
The access to curricular consultation	lijen.cheng@gmail.com name class and question
Expanding teaching and resources	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note	teams