California State University- Sacramento
MBA 264
Business Intelligence with Data Mining
Fall 2014
Monday 6:00PM - 8:50PM
INSTRUCTOR
OFFICE
PHONE
E-MAIL
OFFICE HOURS
Tahoe Hall 1002/1009
Feng "Oliver" Liu, PhD
TAH 2020
(916) 278-7106
liu@saclink.csus.edu
Monday: 11:50 PM - 1:20 PM
2:50 PM - 4:20 PM
Course Description and Objectives
Because of the enormous, and rapidly growing amount of data flowing from, to, and
through enterprises of all sorts, today’s successful enterprises are, typically, those that
make effective use of the abundance of data to which they have access: to make better
predictions, better decisions, and better strategies. This course covers the processes,
methodologies and current practices used to transform business data into useful
information and knowledge for IT-enabled managerial decision support and
performance improvement. The students will learn how to ask the right questions and
how to draw inferences from the data by using the appropriate data mining techniques,
XLMiner/SPSS, and real business data. Overall, the course will enable students to
approach business problems data-analytically, envision data-mining opportunities in
organizations, and also follow up on ideas or opportunities that present themselves.
The specific objectives of the course are:
1. To introduce the steps involved in data mining, from goal definition to model
deployment.
2. To examine supervised learning methods, such as classification and prediction.
3. To understand unsupervised learning methods, such as association rules and
clustering.
4. To discuss data visualization techniques.
5. To improve the student's communication and team working skills.
Students will be afforded the opportunity to demonstrate their abilities in a number of
different ways throughout the course, including individual and group work, written
papers, oral presentations and participation in classroom discussions, among others.
Course Materials
a. Textbook: Data Mining for Business Intelligence, Second Edition, by Galit Shmueli,
Nitin R. Patel and Peter C. Bruce, Wiley; ISBN-10: 0470526823, ISBN-13: 9780470526828.
New copies of this book come bundled with a free 6-month license to use the
software XLMiner, which is required for this course (see below). Since this
software is otherwise expensive, you are recommended to purchase new copies
of the book only.
b. Software: XLMiner. Instructions for obtaining the free 6-month license using the
access code available in the book can be found here:
http://www.solver.com/xlminer/wiley.html.
Since you will need the software for doing in-class exercises as well as homework
assignments, you are advised to install the software on your laptops.
c. Reading Materials: additional articles/papers/cases will be posted in the course
contents page in SacCT.
Grading
HOMEWORK
CLASS PARTICIPATION
PROJECTS
EXAM
45 %
15
20
20
100%
Grading Scale:
A = 93 or higher
B- = 79 – 82
F = < 60
A- = 89 – 92
C = 70-78
B = 83 – 88
D = 60- 69
Homework: These will consist of problem sets that are designed to give the
students valuable practice and enhance their understanding of the concepts covered in
class. These will be individual assignments due by the times and dates designated in the
schedule. Totally there will be ten assignments. If a student misses ONE or more of
these assignments, he or she will lose 5 points. Otherwise, 5 points will automatically be
rewarded to the student just for his or her efforts devoted to the assignments.
The homework will be electronically submitted on SacCT on the due dates (see
Class Schedule). For each student, five of the assignments will be graded (each one is
worth 8 points), and the resulting grades will be documented. The student has the right
to select which ones of hers or his to be graded simply by writing “TBG” (to be graded)
on her or his submissions. More instructions/ guidelines on the assignments will be
given in the classes.
Projects: The purpose of the projects will be for the student to gain hands-on
experience in solving a realistic problem using the data mining principles covered in
class. These will be a group assignment. More specifically, four teams will be formed in
this class. Each team will be assigned two case studies from chapter 18 of the text. The
team is expected to conduct two research projects based on these two cases, and
present its findings, both orally and in writing, by the end of the semester. Detailed
instructions for the projects will be provided later.
Final Exam: This will be a take-home exam—handed out on the last day of class.
This will be an individual assignment due by the time and date designated in the
schedule. Except for extreme cases (i.e., medical emergency), the professor does not
give make-up exams.
Class Participation: The student will be expected to ask questions in class,
participate in class discussions and complete in-class exercises, and will be evaluated
based on her or his involvement in exercises/ discussions in class. This is a subjective
grade assigned by the professor based on both the quality and the quantity of the
student's inputs to class discussions and activities. It also takes into account the
student's attendance to the class.
Policy on Academic Honesty
Cheating devalues your degree. We all have the responsibilities for enforcing a fair and
respectful learning environment. Please take a moment to review the university policies
regarding academic dishonesty:
http://www.csus.edu/umanual/AcademicHonestyPolicyandProcedures.htm.
As a general principle, for individual assignments, there must be no sharing of coding
and the implementation method among individuals in any manners. For group
assignments, there must be no sharing of coding and the implementation method
among groups in any manners. Each assignment is expected to be the unique and
original work of a student or a group for this course in this term. Assignments which
bear strong resemblance to others' will not be graded. Maximum penalty under the
university policy will be enforced for academic dishonesty.
ADA Compliance
It is the policy of California State University to accommodate students with disabilities,
pursuant to federal and state law. Students with disabilities who need classroom
accommodations must make their requests by contacting the Services to Students with
Disabilities (SSWD, http://www.csus.edu/sswd/) at 916‐278‐6955 (phone) 916‐
278‐7239 (TDD), Lassen Hall Room 1008.
CLASS SCHEDULE
Date
Sep 8
Sep 15
Sep 22
Sep 28
Oct 6
Oct 13
Oct 20
Oct 27
Nov 3
Nov 10
Nov 17
Nov 24
Dec 1
Dec 8
Dec 15
Topics
Reading
Syllabus/ Introduction
Research Team Formation and Project
Selection
Overview of Data Mining /
Statistics Refresher (Probability,
Distribution, Hypothesis Testing,
ANOVA, Correlation)/
Introduction to XLMiner
Prediction: MLR
HW #1 due
Classification: Logistic Regression
Ch. 1 & Ch. 2
Class notes
Ch. 6
Ch. 10
Dimension Reduction
HW #2 due
K-Nearest Neighbors
HW #3 due
Naive Bayes
HW #4 due
Decision Trees
HW #5 due
Neural Nets
HW #6 due
Discriminant Analysis
HW #7 due
Project work day
Ch. 4
Association Rules
HW #8 due
Forecasting Time Series
Data Visualization
HW #9 due
Project Presentations
HW #10 due
Project Written Report Due
Ch. 15, 16
Ch. 7
Ch. 8
Ch. 9
Ch. 11
Ch. 12
Ch. 13
Ch. 17
Ch. 3
Final Exam Due: Dec 18
* This is a tentative schedule and may change throughout the semester