Page 3; MBUS673 – Business Intelligence
MBUS 673 (Fall 2014)
Business Intelligence
Professor: Dr. Jason Chen
Class time: W 5:30pm – 8:00pm, Jepson 12 (Nov. 12 - Dec. 17)
E-mail:
Office: Jepson 259
Office hour: T,R: 4-5 pm, W: 3:30-4:30pm; others by appointment
Required text: (Main Text) Turban, Sharda, Delen and King “Business Intelligence”- A Managerial Approach, 2014 (3rd edition); Pearlson, ISBN 13: 9780133051056
Additional Handouts, Readings and Cases will be provided as needed.
Course Description and Goals
Business Intelligence (BI) has become an important agenda for many top executives because they have become extremely aware of its value in providing a competitive differentiator at all levels of the organizations. The course introduces students the concepts, models, architectures, and business applications of BI. Topics include data warehousing, business analytics, business performance management, data, text and web mining. Software applications for business decision makings such as data mining will be introduced for class projects
E-mail Communication – All e-mail communications with me should bear the course and section number (viz: MBUS673) in the subject line, without which the e-mail is likely to be unread. Furthermore, you should include “Dr. Chen” and “your full name” at the beginning and end of the content of each email respectively.
COURSE ATTENDANCE: Students should be aware of the University policy regarding absence. According to the university policy, "[T]he grade given for excessive absence is V, which has the same effect as F and is counted in the GPA....The fact that a student has met other course requirements (such as papers) is not sufficient to change a V to a passing grade." (Catalogue, p. 38). The total time of excessive absence in this class is 300 minutes, equivalent to two absences. In other words, if one student misses two classes for whatever reasons, he/she should expect a V for his/her final grade.
DREAM Students: If you are in the DREAM (Disability Resources, Education and Access) program, it is your responsibility to inform me one week in advance and contact DREM office for arranging EVERY test in their office.
Academic Honesty Policy: The academic honesty policy in the Gonzaga University’s student handbook (p. 145) states that dishonest activities such as cheating, fabrication and plagiarism carry penalties that could lead to severe penalties. Should you have any doubt regarding a course of conduct, don’t hesitate to inquire of me for guidance on addressing the situation. It is considered a violation of “Academic Honesty Policy” if you break the following rule: all class assignments (everything) you turned in must be “Original Work” for this class only” – it means that they are not from someone else or from your other class this semester or before and is considered a violation of “Honesty Policy”.
Internet Resources:
Class materials are available on the Blackboard (https://learn.gonzaga.edu). It is the students’ responsibility to study and check the information on the Internet. Students are also required to participate the Discussion Board (if available) on the Blackboard before the date each chapter is discussed. Please note that it is part of your class performance.
TERM PROJECT AND PRESENTATION:
Term project with presentation is an individual one unless instructor announces it differently. The topic of the project should be in the Business Intelligence with its competitive implications and organizational impact. You should also analyze the case using IS/IT Triangle Strategy model (learned from mbus626). A software implantation using what you learned from the class is highly recommended. The file names for your report and poweroint should be like “MBUS673--title of the topic_Lname_Fname.docx (and .pptx”)
Grading Policy and Evaluation
Your grade will depend on four factors: (NO late assignments will be accepted, consequently, you will be assigned a zero if you turn in late without the instructor’s permission)
Classroom discussions and attendance 20%
Case (Individual) 15%
Software Assignments 20%
Term Project and Presentations ( pptx file) 45%
------
TOTAL 100%
Please note that NO incomplete grade will be assigned unless you obtain a permission from the instructor.
GRADE RANGES:
A / 94% and above / A- / 91% / B+ / 88% / B / 85%B- / 82% / C+ / 79% / C / 75% / C- / 71%
D+ / 68% / D / 65%
MBUS673 SCHEDULE (9-3-2014)
[Individual] / Case/
Term Project
[11]
Nov. 12 / Intro. To the Course
Chapter 1: An Overview of Business Intelligence, Analytics, and Decision Support
Data Mining Software: RapidMiner - Introduction with Decision Tree (1 hr) / AC 1.7: Gilt Groupe’s Flash Sales Streamlined by Big Data Analytics (p.28)
ECC: Nationwide Insurance Used BI to Enhance Customer Service (p.33)
[12]
Nov. 19 / RapidMiner-Market Basket Analysis (with Item Count) (1 hr)
Chapter 2: Data Warehousing / AC 2.1: A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry (p.44)
ECC: Continental Airlines (p.90) No Question#3
[13]
Nov. 26-28 / Thanksgiving HolidayNo Class
[14]
Dec. 3 / Chapter 4: Data Mining Read the following pages only: pp. 145-171
p. 176 (Decision Trees only)
pp. 186-195
RapidMiner-Market Basket Analysis (Ignores Item Amounts)
HW
Term Project Proposal Due
(You will learn the tool in the class) / AC 4.6: Data Mining Goes to Hollywood! (pp.189-192)
ECC: NO. / Term project proposal (Phase I)
[15]
Dec. 10 / Chapter 6: Big Data and Analytics
Decision Making Software (for Business Performance Management): AHP
Case discussion / AC 6.4: Big Data and Analytics in Politics (p.301)
ECC: Discovery Health Turns Big Data into Better Healthcare (p.323) / All students;
Term project (Phase II)
[16]
Dec. 17 / Term Project (Paper) PRESENTATION / All students;
Term project presentation
* Note that the number within the [ ] denotes the week number.
* AC: Application Case on each chapter
*ECC: End of Chapter Case on each chapter
Term Project and Description
A topic of your term project can be selected from an industry with a business application such as market basket analysis. You need to receive an approval from the instructor if you do not use “market basket analysis” for your business example..
The project can be a general survey paper of Business Intelligence or a research-oriented paper with a business focus. An example of using market basket analysis or relevant BI technique (approved by the instructor) should be included in the paper to support the main theme described in the paper.
Outline of the term paper:
Title of the term paper
1. Introduction
2. Title (Main Body) (that you have to break down into several sections)
i. Subtitle-1
ii. Subtitle-2
iii. …
3. An Example: (Market Basket)
a) Case description (a Dataset should be included in the paper – captured from the RapidMiner or copy/paste from the Excel sheet)
b) Objective from the case
c) Findings/Analysis and Business Implication
d) Data set about the Market Basket:
i. The data may be from a real business application or you can make-up it and it should be related to the topic of the paper.
ii. The scenario should have at least six (6) customers and ten (10) different items
iii. The dataset should include three columns (with itemcount) and use “name” not “number” so that it provides a better format for analysis and interpretation.
e) Analysis on the outputs
4. Conclusion
5. References
Size of the paper: at least 10 pages (both quality and quantity should be balanced) double space and with Times New Roman and font size of 12.
Please note that when you upload term project, three files should be included: 1) Word (paper), 2) EXCEL , and 3) powerpoint files. You are allowed to upload them only once.