國立中山大學 111學年度第1學期 課程教學大綱

National Sun Yat-sen University 111Academic year1st Semester Course syllabus

中文名稱
Course name(Chinese)

商業數據分析實務

課號
Course Code

IB533

英文名稱
Course name(English)

THE PRACTICE OF BUSINESS ANALYTICS

課程類別
Type of the course

講授類

必選修
Required/Selected

選修

系所
Dept./faculty

管理學院國際經營管理碩士學程

授課教師
Instructor

卓雍然    

學分
Credit

3

因應嚴重特殊傳染性肺炎(武漢肺炎),倘若後續需實施遠距授課,授課方式調整如下:

         同步遠距【透過網路直播技術,同時進行線上教學,得採Microsoft Teams、Adobe connect等軟體進行】
同步遠距含錄影【透過網路直播技術,同時進行線上教學並同時錄影,課程內容可擇日再重播,得採Microsoft Teams、Adobe connect等軟體進行】
非同步遠距【課堂錄影或錄製數位教材放置網路供學生可非同時進行線上學習,得採EverCam、PPT簡報錄影、錄音方式進行】
實作類課程,經評估無法採遠距課程教學,後續復課後密集補課

★遠距教學軟體操作說明連結

因應嚴重特殊傳染性肺炎(武漢肺炎),倘若後續需實施遠距授課,評分方式調整如下:

        
1.Class Participation15%
2.Personal Assignments30%
3.Group Assignments20%
4.Final Project20%
5.Cross Rating15%

課程大綱 Course syllabus

         本課程教學大綱已提供完整英文資訊(本選項僅供統計使用,未提供完整英文資訊者,得免勾記)【Provide information of course syllabus in English.(This is for statistical use only. For those who do not provide information of course syllabus in English, do not check this field.)】

【Course Description】
Capitalizing the business school’s Big Data Business Analytics Platform (https://bap2.cm.nsysu.edu.tw/), in this course we cover: (1) R Language Basics, (2) Data Exploration and Visualization, (3) Application of Probability & Statistics, (4) Applicational R-Packages, and (5) Practical Strategic Planning. Besides the aforementioned topics of business analysis, this course also prepares the students for advanced technical courses related to big data, machine learning and artificial intelligence in the General Management, Digital Marketing and FinTech Micro Curriculums in the ITSA Program (https://bap2.cm.nsysu.edu.tw/?page_id=513).
【Course Web Page】 https://bap2.cm.nsysu.edu.tw/?page_id=1558
【Prerequisite】
Although there is no mandatory prerequisites, basic knowledge of probability (high school level) is presumed. As for statistics, we will align with the class of IB502 (110.1) - Statistics and Quantitative Methods. The course loading is quite heavy. For those who do not have programming experience, it'd take 6 ~ 10 hours per week to finish the personal and team assignments.
【Course Outlines】
Course Intro. - Business Analytics in R
PART-I: DATA & PROGRAMMING
..01 Intro. R and RStudio
..02 Cases: Solving Business Problems by Data Manipulation
..03 Descriptive Analysis with Simple Plots
..04 Cases: Exploring Data by Comparison
PART-II: APPLICATIONAL PROBABILITY
..05 Applicational Probability in R
..06 Case: Data, Model, Prediction, Decision
..07 Case: Analyze Marketing Research Data
PART-III: DATA EXPLORATION
..08 Explorative Analysis Methods
..09 Data Visualization Techniques
..10 Cases: Clustering and Dimension Reduction
..11 Case: Retail POS Data
PART-IV: PREDICTIVE MODELS
..12 Predicting Quantity, Linear Regression
..13 Predicting Probability, Logistic Regression
..14 Case: Customer Value Management
PART-V: BUSINESS LOGIC
..15 From Decision to Prediction
..16 Assumption and Simulation
..17 Performance Evaluation and Optimization
..18 Capstone Project: Data Driven Marketing Plan


課程目標 Objectives

         【Objectives】
1. Introduce to programming (R) language. Overcome the entry barrier of programming language with interactive notebooks, web-pages and web-based simulation tools.
2. Develop major business analytics skills in practical data cases.
3. Practice and experience the synergy among programming language, statistics and managerial knowledge.
4. Prepare the advanced analytics courses that involve big-data, ma-chine learning and/or artificial intelligence.



授課方式 Teaching methods

         【Course Format】
The Time Allocation would be : Lecture 48 Hours + Project Presentation 6 Hours
Considering that some students do not have programming experience. We will impose mixed-specialty grouping in the beginning of the semester. In addition to the lecture, we will also have a TA session every weeks, in which TA can offer assistance to the teams.



評分方式﹝評分標準及比例﹞Evaluation (Criteria and ratio)等第制單科成績對照表 letter grading reference

        
1.Class Participation15%
2.Personal Assignment30%
3.Group Assignment20%
4.Final Project20%
5.Cross Rating15%

參考書/教科書/閱讀文獻 Reference book/ textbook/ documents
〔請遵守智慧財產權觀念,不可非法影印。教師所提供之教材供學生本人自修學習使用,不得散播及做為商業用途〕
No copies for intellectual property rights. Textbooks provided by the instructor used only for self-study, can not broadcast or commercial use

         We will use self-developed materials, including web pages, program notebooks, app's and presentation files. No text book is required. There is an online free reference book as below:
Hadley Wickham and Garrett Grolemund, R for Data Science, O'REILLY 2016 (https://r4ds.had.co.nz/)



彈性暨自主學習規劃 Alternative learning periods

本門課程是否有規劃實施學生彈性或自主學習內容(每1學分2小時)
Is any alternative learning periods planned for this course (with each credit corresponding to two hours of activity)?
否:教師需於「每週課程內容及預計進度」填寫18週課程進度(每1學分18小時之正課內容)。
No:The instructor will include an 18-week course plan in the weekly scheduled progress (each credit corresponds to 18 hours of instruction)
是:教師需於「每週課程內容及預計進度」填寫16週課程內容(每1學分16小時之正課內容),並於下列欄位填寫每1學分2小時學生彈性或自主學習內容。
    Yes:The instructor will include a 16-week course plan in the weekly scheduled progress (each credit corresponds to 16 hours of instruction);the details of the planned alternative learning periods are provided below (each credit corresponds to two hours of activity).

學生彈性或自主學習活動
Alternative learning periods
勾選或填寫規劃內容
Place a check in the appropriate box or provide details
時數
Number of hours
學生分組實作及討論
Group work and discussion
參與課程相關作業、作品、實驗
Participation in course-related assignments, work, or experiments
參與校內外活動(研習營、工作坊、參訪)或競賽
Participation in on- or off-campus activities (e.g., seminars, workshops, and visits) or competitions
課外閱讀
Extracurricular reading
線上數位教材學習
Learning with online digital learning materials
其他(請填寫規劃內容)
Other (please provide details)

每週課程內容及預計進度 Weekly scheduled progress

        
週次日期授課內容及主題
WeekDateContent and topic
12022/09/04~2022/09/10Business Analytics in R, Intro. R and RStudio
22022/09/11~2022/09/17Cases: Solving Business Problems by Data Manipulation
32022/09/18~2022/09/24Descriptive Analysis with Simple Plots
42022/09/25~2022/10/01Cases: Exploring Data by Comparison
52022/10/02~2022/10/08Applicational Probability in R
62022/10/09~2022/10/15Case: Data, Model, Prediction, Decision
72022/10/16~2022/10/22Case: Analyze Marketing Research Data
82022/10/23~2022/10/29Explorative Analysis Methods
92022/10/30~2022/11/05Data Visualization Techniques
102022/11/06~2022/11/12Cases: Clustering and Dimension Reduction
112022/11/13~2022/11/19Case: Retail POS Data
122022/11/20~2022/11/26Predicting Quantity, Linear Regression
132022/11/27~2022/12/03Predicting Probability, Linear Regression
142022/12/04~2022/12/10Case: Customer Value Management
152022/12/11~2022/12/17Confusion and Payoff Matrix
162022/12/18~2022/12/24Assumption and Simulation
172022/12/25~2022/12/31Performance Evaluation and Optimization
182023/01/01~2023/01/07Capstone Project: Data Driven Marketing Plan

課業討論時間 Office hours

         時段1 Time period 1:
時間 Time:星期一1700~1900
地點 Office/Laboratory:4091-1
時段2 Time period 2:
時間 Time:星期三1700~1900
地點 Office/Laboratory:4091-1

系所學生專業能力/全校學生基本素養與核心能力 basic disciplines and core capabilitics of the dcpartment and the university

        
系所學生專業能力/全校學生基本素養與核心能力
basic disciplines and core capabilities of the department and the university
課堂活動與評量方式
Class activities and evaluation
本課程欲培養之能力與素養 This course enables students to achieve.紙筆考試或測驗 Test.課堂討論︵含個案討論︶ Group discussion (case analysis).個人書面報告、作業、作品、實驗 Indivisual paper report/ assignment/ work or experiment.群組書面報告、作業、作品、實驗 Group paper report/ assignment/ work or experiment.個人口頭報告 Indivisual oral presentation.群組口頭報告 Group oral presentation.課程規劃之校外參訪及實習 Off-campus visit and intership.證照/檢定 License.參與課程規劃之校內外活動及競賽 Participate in off-campus/ on-campus activities and competitions.
課外閱讀 Outside reading.
※系所學生專業能力 Basic disciplines and core capabilities of the department
1.學生能夠辨識和理解國際經營管理環境中的跨文化問題,並能與不同文化背景者進行有效溝通。 1. Students are able to identify and understand cross-cultural issues as they arise in an international management context and can communicate effectively with people from different cultural backgrounds. V VVVVV V  
2.學生能了解國際商業環境中的倫理和社會責任問題,並能從多個利益關係者的角度加以批判性分析。 2. Students are aware of ethical and social responsibility issues that may arise in an international business context and are able to critically analyze these issues from the perspectives of multiple stakeholders.           
3.學生能以當代管理理論為基礎,辨別和分析全球商業環境中的關鍵性管理議題與挑戰。 3. Based on a strong foundation of contemporary management theories, students are able to identify and analyze key management challenges in the global business environment.V VVVVV V  
4.學生具有全球視野的商業經營能力,並具備對區域和地方問題的認識與關懷。 4.Students develop a global vision on doing business, but maintain an awareness of regional and local issues.            
5.學生能發展批判性思考和問題解決的能力。 5.Students develop critical thinking skills and problem-solving abilities.           
※全校學生基本素養與核心能力 Basic disciplines and core capabilities of the university
1.表達與溝通能力。 1. Articulation and communication skillsV VVVVV    
2.探究與批判思考能力。 2. Inquisitive and critical thinking abilities           
3.終身學習能力。 3. Lifelong learningV       V V
4.倫理與社會責任。 4. Ethnics and social responsibility           
5.美感品味。 5. Aesthetic appreciation           
6.創造力。 6. Creativity           
7.全球視野。 7. Global perspective           
8.合作與領導能力。 8. Team work and leadership           
9.山海胸襟與自然情懷。 9. Broad-mindedness and the embrace of nature            

本課程與SDGs相關項目:The course relates to SDGs items:

        
SDG1-消除貧窮(No Poverty)
SDG2-消除飢餓 (Zero Hunger)
SDG3-良好健康與福祉(Good Health and Well-being)
SDG4-教育品質(Quality Education)
SDG5-性別平等(Gender Equality)
SDG6-乾淨水源與公共衛生(Clean Water and Sanitation)
SDG7-可負擔乾淨能源(Affordable and Clean Energy)
SDG8-優質工作與經濟成長(Decent Work and Economic Growth)
SDG9-工業、創新和基礎建設(Industry,Innovation and Infrastructure)
SDG10-減少不平等(Reduced Inequalities)
SDG11-永續城市(Sustainable Cities and Communities)
SDG12-責任消費與生產(Responsible Consumption and Production)
SDG13-氣候行動(Climate Action)
SDG14-海洋生態(Life Below Water)
SDG15-陸域生態(Life on Land)
SDG16-和平、正義和穩健的制度(Peace,Justice And Strong Institutions)
SDG17-促進目標實現的全球夥伴關係(Partnership for the Goals)
本課程和SDGS無關

本課程校外實習資訊: This course is relevant to internship:

         本課程包含校外實習(本選項僅供統計使用,無校外實習者,得免勾記)
The course includes internship.(For statistical use only. If the course without internship, please ignore this item.)

實習定義:規劃具有學分或時數之必修或選修課程,且安排學生進行實務與理論課程實習,於實習終了取得考核證明繳回學校後,始得獲得學分;或滿足畢業條件者。(一般校內實習請勿勾選此欄位)

Internship: The required or elective courses should include credits and learning hours. Students should participate in the corporative company or institution to practice and learn the real skills. An internship certification must be handed in at the end of internship to get the credits or to fulfil the graduation requirements.

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