SemesterSpring Semester, 2020
DepartmentSelective courses of undergraduate level,College of Commerce
Course NameBusiness Analytics: Marketing and Decisions
Course TypeSelectively
PrerequisiteBasic Statistics、Statistics
Course Objective
Course Description
Course Schedule

We will have a total of 16 class meetings. Below is the tentative schedule.



R Introduction & Exploratory Data Analysis

W1: 2/19

R Introduction & Exploratory Data Analysis

W2: 2/26

Market Basket Analysis & Recommendation

W3: 3/04

Network Analysis & Case Demonstration

W4: 3/11

Clustering & Customer Segmentation

W5: 3/18

Linear Regression & Price Prediction

W6: 3/25

Linear Regression & Price Prediction

W7: 4/01

Generalized Linear Models & Customer Retention

W8: 4/08

Generalized Linear Models & Customer Retention

W9: 4/15

Generalized Linear Models & Customer Retention

W10: 4/22

Tree-Based Methods

W11: 4/29

Black-Box Machine Learning

W12: 5/06

Black-Box Machine Learning

W13: 5/13

Text Mining & User Generated Content

W14: 5/20

Project Preparation (No Class Meeting)

W15: 5/27

Project Demo

W16: 6/03

Project Demo

W17: 6/10

Final Report Due (No Class Meeting)

W18: 6/17


Teaching Methods
Teaching Assistant



Homework: 40%

    We will distribute 4 to 5 assignments during the semester. While you are allowed to discuss homework questions with classmates, you must finish all assignments by yourself.

Midterm Exam: 25%

    We will explain the exam logistics in detail. No make-up exam can be scheduled without prior arrangements.

Term Project: 35%

    You have to form a group of 3-4 people to work on this. The project will require you to identify one or multiple questions and apply analysis technique(s) learnt from this course. More details regarding the term project will be discussed as the course proceeds.

Each group will make a final presentation of their term project on June 03 and June 10. All registered students should come to class and see what other groups have done. For the presentation, please consider yourselves as business analysts who have to show their effort in a logical/clear fashion and convince the audience that their investigation is useful.


In addition, your group has to turn in a report written in ADEQUATE Chinese or English. The report must 1) articulate the research question, 2) explain the method/model you use to tackle the question, 3) specify data source and variables, 4) show exploratory data analysis, 5) present results of model-based analysis, and 6) discuss implications of research findings.

    The written report is going to be due on June 17. Please e-mail us the report in .pdf and the R code. Do keep the report short and sweet.

Textbook & Reference

Lecture notes and assigned readings will be provided. So NO textbooks are required. Below lists our key references in developing this course.

Chapman & Feit 2015. R for Marketing and Analytics (e-copy available from NCCU library website).

Shmueli et al. 2017.  Data Mining for Business Analytics.

Bertsimas et al. 2016. The Analytics Edge.

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