|
|
課程簡介 Course Introduction
|
|
開課年度學期 Year / Term
|
114 學年度 第 1 學期
|
|
開課班級 Department
|
資訊工程學系 資工四、碩合選
|
|
授課方式 Instructional Method
|
課堂教學 、 英語-不加成
|
|
課程電腦代號 Course Reference Number
|
159031
|
|
課程名稱(中文) Course Title(Chinese)
|
資料探勘
|
|
課程名稱(英文) Course Title(English)
|
Data Mining
|
|
學分數/時數 Credit Hours
|
3 /
3
|
|
必(選)修 Requirement / Elective Course
|
選修
|
|
授課老師 Instructor
|
蘇溢芳
|
|
助教 Teaching Assistant
|
|
|
上課時間 Meeting Time
|
星期五，節次3、4、5
|
|
上課教室 Classroom
|
ZA205
|
|
Office Hours
|
|
| 獲獎及補助情形 Awards and Grants |
|
|
| 聯合國永續發展目標 (SDGs跨域類別) Sustainable Development Goals, SDGs |
|
|
|
課程目標 Learning Objectives
|
在數據驅動的商業環境中，資料探勘已成為企業獲得競爭優勢的核心能力。本課程以商業問題解決為導向，透過真實的企業案例與實作專案，培養學生運用資料探勘技術創造商業價值的實務能力。
學生將學習如何從商業角度思考數據問題，包括客戶行為分析、市場區隔、風險評估、推薦系統等關鍵應用領域。課程強調從原始數據到商業洞察的完整流程：問題定義、數據收集與清理、探索性分析、模型建構、結果解釋，以及將分析結果轉化為可執行的商業策略。
除了掌握分類、分群、關聯分析等核心技術外，學生也將培養跨部門溝通能力，學習如何向非技術背景的管理層和業務團隊清楚傳達數據洞察，並評估分析專案的投資報酬率(ROI)。透過業界常用的Python工具與雲端平台實作，確保學生具備立即投入職場的實務技能。
In today's data-driven business world, knowing data mining is a must-have skill for companies wanting to get ahead. This course is carefully designed with a strong focus on solving real business problems. We'll use actual company examples and lots of hands-on projects. Our main goal is to help students truly use data mining techniques to create real business value.
Students will learn to look at data problems from a business point of view. We'll cover key areas like understanding customer behavior, dividing markets into groups, checking for risks, and building recommendation systems. A big part of this course is going through the whole process, from raw data to useful business ideas. This includes clearly defining the problem, collecting and cleaning data well, exploring data to find insights, building smart models, clearly explaining results, and most importantly, turning those findings into practical business plans.
Beyond learning essential techniques such as classification, clustering, and association analysis, students will also get better at talking across different teams. They'll learn how to explain complex data insights simply to managers and business people who aren't technical. They'll also learn how to figure out if data projects are worth the money (ROI). By getting plenty of practice with common Python tools and popular cloud platforms, this course makes sure students have the practical skills they need to start working right away.
|
|
先修 ( 前置 ) 課程 Prerequisite
|
|
Python
|
| 彈性教學規劃 Flexible Teaching/Planning Schedules |
| ＊本課程實施16+2週彈性教學方案，其中第17、18週之彈性規劃如下： |
|
線上教學/討論
|
|
自主學習
|
|
|
課程大綱 Course Syllabus
|
| 週次 Week |
課程單元大綱 Unit |
教學方式 Instructional Method/Style/Teaching Style |
參考資料或相關作業 References or Related Materials |
評量方式 Grading |
|
1
|
Overview of this course
|
Lectures and hands-on practice
|
|
|
|
2
|
Simple linearregression/簡單線性回歸
|
Lectures and hands-on practice
|
|
|
|
3
|
Simple linearregression Case study
|
Lectures and Case study
|
|
|
|
4
|
Multiple linearregression/複回歸介紹
|
Lectures and hands-on practice
|
|
|
|
5
|
National Holiday
|
|
|
|
|
6
|
Multiple linearregression Case study
|
Lectures and Case study
|
|
|
|
7
|
National Holiday
|
|
|
|
|
8
|
Decision Tree and Case Study
|
Lectures and hands-on practice
|
|
|
|
9
|
Midterm
|
Written exam
|
|
|
|
10
|
Condition probability 條件機率
|
Lectures and hands-on practice
|
|
|
|
11
|
Support vecor machine(SVM) 支持向量機
|
Lectures and hands-on practice
|
|
|
|
12
|
SVM Case Study
|
Lectures and hands-on practice
|
|
|
|
13
|
K-means 聚類技術
|
Lectures and hands-on practice
|
|
|
|
14
|
K-means 聚類技術II
|
Lectures and hands-on practice
|
|
|
|
15
|
Kmeans – Case Study
|
Lectures and hands-on practice
|
|
|
|
16
|
Kmeans – Case StudyII
|
Lectures and hands-on practice
|
|
|
|
17
|
Final Project Design
|
|
|
|
|
18
|
Final Project Implemetation
|
|
|
|
|
單一課程對應校能力指標程度 The Degree to Which Single Course Corresponds to School Competence
|
| 編號 No. |
校核心能力 School Core Competencies |
符合程度 Degree of conformity |
|
1
|
公民力 (Citizen)
|
1
|
|
2
|
自學力 (Self-learning)
|
3
|
|
3
|
資訊力 (Information)
|
4
|
|
4
|
創造力 (Creativity)
|
3
|
|
5
|
溝通力 (Communication)
|
2
|
|
6
|
就業力(Employability)
|
4
|
|
單一課程對應系能力指標程度 The Degree to Which Single Course Corresponds to Department Competence
|
| 編號 No. |
類別 Category |
系核心能力 Department Core Competencies |
符合程度 Degree of conformity |
|
01
|
系所
|
具備資訊工程領域之基本知識及程式設計能力
|
5
|
|
02
|
系所
|
擁有資訊軟體及硬體系統設計、實作、整合及管理的能力
|
5
|
|
03
|
系所
|
運用數學強化邏輯性思考，增進處理資訊工程問題的能力
|
3
|
|
04
|
系所
|
具有獨立思考並自行解決問題的能力
|
3
|
|
05
|
系所
|
自動發現問題並主動蒐集、分析資料，達成自我學習的能力
|
3
|
|
06
|
系所
|
維持良好人際互動、溝通與團隊合作的能力
|
1
|
|
07
|
系所
|
訓練足夠抵抗環境壓力與時間管理的能力
|
2
|
|
08
|
系所
|
資訊工程倫理及實務之歸納評比及實務能力與表達能力
|
1
|
|
09
|
系所
|
掌握資訊科技之國際變化趨勢
|
4
|
|
10
|
系所
|
明瞭國內外資訊產業與社會發展的能力
|
4
|
|
單一課程對應院能力指標程度 The Degree to Which Single Course Corresponds to College Competence
|
| 編號 No. |
院核心能力 College Core Competencies |
符合程度 Degree of conformity |
|
1
|
語文能力
|
3
|
|
2
|
溝通與合作能力
|
1
|
|
3
|
創新與實踐能力
|
4
|
|
4
|
專業知能
|
5
|
|
教科書或參考用書 Textbooks or Reference Books
|
|
館藏書名 Library Books
|
|
備註 Remarks
|
Deep learning
作者 : Ian Goodfellow , Yoshua Bengio , Aaron Courville 著 陳仁和 譯
中文出版社 : 碁峰資訊 英文出版社：The MIT Press
|
※請尊重智慧財產權，不得非法影印教科書※
※ Please respect intellectual property rights and do not illegally photocopy textbooks. ※
|
教學方法 Teaching Method
|
|
教學方法 Teaching Method
|
百分比 Percentage
|
|
講述
|
60 %
|
|
個案研討
|
30 %
|
|
問題導向學習
|
10 %
|
| 總和 Total |
100 % |
|
成績評量方式 Grading
|
| 評量方式 Grading |
百分比 Percentage |
|
期中考
|
40 %
|
|
期末考
|
60 %
|
| 總和 Total |
100 % |
|
成績評量方式補充說明
|
期末考是以project 的方式呈現, 考試內容與型態將於課堂中說明
|
|
課程大綱補充資料 Supplementary Material of Course Syllabus
|
|
|
|