教學大綱 Syllabus

科目名稱:商情預測

Course Name: Business Forecasting

修別:選

Type of Credit: Elective

3.0

學分數

Credit(s)

20

預收人數

Number of Students

課程資料Course Details

課程簡介Course Description

This course introduces essential business analytics which include:

  • R basics through RStudio,
  • Generating reproducible reports with R Markdown,
  • Data manipulation (with the dplyr package in R),
  • Data visualization (with the ggplot2 package in R),
  • Modelling business problems with graphical models (with the causact package in R),
  • Translating graphical models into computer codes (with the causact, greta and TensorFlow packages in R) to generate insights.

Note: the second half of this course involves heavy simulation with computer packakes which only works on a laptop/pc with a Pentium CPU. A laptop/pc with a non-Pentium CPU, such as Apple or AMD will NOT work. 

核心能力分析圖 Core Competence Analysis Chart

能力項目說明


    課程目標與學習成效Course Objectives & Learning Outcomes

    Students will learn essential skills in business analytics and demonstrate proficiency in 

    1. aligning computational and mathematical models with real-world scenarios;
    2. communicating with and leveraging the expertise of business stakeholders while using modern software stacks and statistical workflows.
    3. presenting generated-insights back to stakeholders.

    After completing this course, students will be equipped with the ability

    • to easily travel between the real-world of business and the theoretical world of Statistics,
    • to translate real-world scenarios into both statistical and computational representations that yield actionable insight, and
    • to take that insight back to the real-world to persuade stakeholders to alter and improve their real-world decisions.

    每周課程進度與作業要求 Course Schedule & Requirements

    教學週次Course Week 彈性補充教學週次Flexible Supplemental Instruction Week 彈性補充教學類別Flexible Supplemental Instruction Type
    週次
    Week
    日期
    Date
    課程主題
    Course Theme
    課程內容與指定閱讀
    Content and Reading Assignment
    教學活動與作業
    Activity and Homework
    1 2025/2/20 課程簡介 & R Basics Lecture Notes &Chap. 4,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    2 2025/2/27 R Basics Lecture Notes &Chap. 4,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    3 2025/3/6 R Basics Lecture Notes &Chap. 4,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    4 2025/3/13 R Basics Lecture Notes &Chap. 4,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    5 2025/3/20 Data Manipulation  Lecture Notes &Chap. 6,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    6 2025/3/27 Data Visulization Lecture Notes &Chap. 8~9,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    7 2025/4/3 國定假日 不上課 NA
    8 2025/4/10 Data Visulization Lecture Notes &Chap. 8~9,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    9 2025/4/17 期中考試 不上課 課堂考試
    10 2025/4/24 Graphical Models Lecture Notes &Chap. 12,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    11 2025/5/1 Bayesian Inference Lecture Notes &Chap. 13,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    12 2025/5/8 Generative DAGs (directed acyclic graphs) Lecture Notes &Chap. 14~15,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    13 2025/5/15 Generative DAGs (directed acyclic graphs) Lecture Notes &Chap. 14~15,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    14 2025/5/22 Decisions and Actions Under Uncertainty Lecture Notes &Chap. 24,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    15 2025/5/29 Decisions and Actions Under Uncertainty Lecture Notes &Chap. 24,
    Fleischhacker (2022)
    教師講授及討論/ Assignment TBA  
    16 2025/6/5 自主總整學習 不上課 NA
    17 2025/6/12 自主總整學習 不上課 NA
    18 2025/6/19 期中考試 不上課 課堂考試

    授課方式Teaching Approach

    70%

    講述 Lecture

    20%

    討論 Discussion

    0%

    小組活動 Group activity

    10%

    數位學習 E-learning

    0%

    其他: Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    • Attendance & Participation: 20%.

      • This is not a subject which you can skip sessions here and there and still be able to catch up and finish the subject satisfactorily.

    • Assignments: 20%.

      • There will be 4~6 assignments to be announced.

    • Midterm Exam (2025/04/17): 30%.

    • Final Exam (2025/06/19): 30%.

       

    指定/參考書目Textbook & References

    • I am going to be relying more on classroom presentations and pdf handouts to teach this course than on any one textbook. Hence, there will be mo designated any textbook for this course.
    • However, there is a nice book that can serve as a useful supplementary reference book for this course.

    Adam Fleischhacker (2020) A Business Analyst's Guide to Business Analytics, which can be accessed via the URL: https://causact.com/, also with YouTube videos that accompany each chapter (https://youtube.com/playlist?list=PLassxuIVwGLPy-mtohX-NXrjD8fc9FBOc).

     

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

    維護智慧財產權,務必使用正版書籍。 Respect Copyright.

    本課程可否使用生成式AI工具Course Policies on the Use of Generative AI Tools

    本課程無涉及AI使用 This Course Does Not Involve the Use of AI.

    課程相關連結Course Related Links

    
                

    課程附件Course Attachments

    課程進行中,使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    需經教師同意始得使用 Approval

    列印