Download Syllabus

Bigdata and Business Intelligence
Extended Syllabus for
(2014 2nd Semester)
Course
Title
Bigdata and Business Intelligence
Credit
3 Credits
Class
Time
Mon 15:00-16:15, Wed 16:30-17:45
Course
Number
Enrollment
Eligibility
MGT4226
No prerequisite
Classroom
Homepage:
hompi.sogang.ac.kr/ jinhwakim/csm
Name: Jinhwa Kim
Instructor
E-mail: [email protected]
Telephone: 02-705-8860
's
Photo
Office: PA 713
Office Hours: M, T, W, Th 13:00-15:00 and by appointment
1. SBS Mission
1) To provide outstanding education grounded in Jesuit tradition that cultivates students to
become responsible leaders of the global business through a developed contribution to
mankind, and
2) To create new knowledge necessary for advancement of the academic world by
emphasizing top-quality scholarship and research.
2. SBS Vision
“A Leading Business School in Asia”
3. SBS Values and Related AoL Assessment Traits
 Globalization: The School expands its global student and faculty network.
 Leadership: The School fosters the spirit of leadership in its stakeholders.
 Excellence: The School commits itself to the pursuit of excellence in education and
research.
 Ethics: The School upholds ethics as its mode of integrity and credibility.
. G: Globalization
. L: Leadership [ AoL id# 2-3 a, b; 2-5a, b, c]
. E: Excellence [ AoL id# 3-3a, b; 3-4; 3-5]
. E: Ethics [ AoL id# 4-2]
Ⅰ. Course Overview
1
1. Description
The major objective of this course is for the student to analyze diverse data sets using data
mining techniques. Major topics in this class are customer data, cluster analysis, data
visualization, association rules analysis, decision tree analysis, text and web mining, SNS
data mining et al.
2. Prerequisites
None
3.
4.
Course Format (%)
Lecture
Discussion
Experiment/Practicum
80%
10%
8%
Field study
Presentations
%
Other
2%
%
Evaluation (%)
mid-term
Exam
40%
Final exam
40%
Quizzes
%
Presentations
Projects
2%
8%
Assignments
Participation
10%
%
Other
%
Ⅱ. Course Objectives
The major objective of this course is for the student to analyze the data sets on customer
behavior and design/model service for the customers. Students learn to analyze and
understand customer behavior by analyzing the customer data. They also learn how to
design and model customer service based on the analysis and design. By the time a student
completes this course, he or she should be able to understand:
1) Major tools in big data, business intelligence, and data mining.
2) The processes of analyzing big data.
3) Theories of big data.
4) Analytical software to analyze big data
5) Major applications of big data
6) Value creations with big data.
Ⅲ. Course Format
(* In detail)
You are expected to have read and prepared the class material for the lecture before the
class. Some course topics are sequential. The purpose of class lecture is both to explain and
to supplement the text and assigned materials. Lecture is not a substitute for reading/studying
the assigned material(s). You will spend time in computer labs working on exercises in the
lab and offering solutions, which illustrate the theories being covered in class and homework
assignment.
Homework will be assigned in order for you to become familiar with the theories
covered the class. Homework will be discussed in class, so all students are expected to
participate in the discussions. Homework will be collected at the beginning of each due
date and will be returned. Each problem in homework will be graded based on your
genuine attempt to answer the questions, not the correctness of the answers. Late
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submission will not be accepted unless you are absent from due date with acceptable
documentation, but you may turn in early if you must miss class.
Ⅳ. Course Requirements and Grading Criteria
There are two tests, 8 assignments, 1 team project. All tests will be multiple choices.
You will have your own team for your team project. This team may/will service for study
group for the course. Some assignments are case summary for given cases and some are
computer assignments using software on bigdata.
The course follows grading policy by the School of Business. We have a curve
meaning fixed percent of A, B, and C.
Ⅴ. Course Policies
Class attendance is mandatory. Also, disruptive behaviors such as arriving late, leaving
early without permission, chatting, or using any type of electronic device, etc. will not be
tolerated. You can earn a passing grade only if you regularly attend and participate in class.
You will be given a warning for the first disruptive behavior and you are asked to leave the
class after the second warning.
Ⅵ. Materials and References
<Textbook>
. None, course material is available in the class web site.
<Lecture Note>
. PPT Lecture assignments will be posted in the class web site.
<Reference book>
. Introduction to Business Data mining by David Olson and Yong Shi, McGraw Hill 2007,
ISBN 007-124470-0
Ⅶ. Course Schedule
(* Subject to change)
Learning
Objectives
Week
1
Topics
Class Work
(Methods)
Understand what bigdata and business intelligence are
Overview of course
What are bigdata and business intelligence
Lecture, demo and discussion,
3
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
2
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
3
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
4
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Week
Learning
Objectives
Bigdata, business intelligence, data mining
none
Understand the theories and cases of Association rule analysis
(1)
Association rule analysis
Association rule analysis
Lecture Note on Association rule analysis
Assignment # 1 (case #1 on association rule analysis)
Learn E-Miner for association rule analysis
E-Miner
Lab
E-Miner for association rule analysis
Assignment # 2
: Create output from E-Miner for association rule analysis
Learn theories and cases on Cluster analysis and Data
visualization
2)
Cluster analysis and Data visualization
Lecture, demo and discussion
Lecture note on Data visualization
Assignment #3
: A case on data visualization
Understand the theories and cases on Case-based reasoning
4
5
Topics
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
6
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
7
3)
Case-based reasoning
Lecture, demo and discussion
Lecture notes on Case-based reasoning
Assignment #4 on Case-based reasoning
Understand theories and cases on Cluster analysis
4)
Cluster analysis
Lecture, demo and discussion
Lecture notes on Cluster analysis
Assignment #5 on Cluster analysis
Understand theories and cases on decision tree
5)
Decision tree
Class Work
(Methods)
Lecture, demo and discussion
Materials
(Required
Readings)
Lecture notes on Decision tree
Assignments
Assignment #6 on decision tree
Learning
Objectives
Week
8
Topics
Midterm Exam
Class Work
(Methods)
5
Materials
(Required
Readings)
Assignments
Learning
Objectives
Week
9
Topics
Lab on decision tree
Class Work
(Methods)
Lab on decision tree
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
10
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
11
Learn to use E-Miner for decision tree analysis
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
E-Miner for decision tree
Assignment #7 on the lab from decision tree with E-Miner
Learn theories and cases on Neural networks
6)
Neural networks
Lecture, demo and discussion
Neural networks
Assignment #8 on Neural networks
Learn theories and cases on Text mining
7)
Text mining
Lecture, demo and discussion
Lecture notes on Text mining
Assignment #9 Text mining
6
Week
12
Learning
Objectives
Lab on text mining
Topics
Lab on text mining
Class Work
(Methods)
Lab on text mining
Materials
(Required
Readings)
Lab on text mining
Assignments
Learning
Objectives
Topics
Week
13
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Learning
Objectives
Topics
Week
14
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Week
15
Learning
Objectives
Topics
Assignment #10 on text mining
Learn theories and cases on on Web mining & SNS Mining
8)
Web mining & SNS Mining
Web mining & SNS Mining
Lecture notes on Web mining & SNS Mining
Assignment #11 on Web mining & SNS Mining
Learn theories and cases on Web mining & SNS Mining
Prepare team project in the lab: team meeting for co-work on it
Web mining & SNS Mining
Team project in the lab
Lecture and lab for the project
Lecture notes on Web mining & SNS Mining
none
Team Work Presentations for the final team project
Project Presentations
Review for the final exam
7
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Project Presentations and submission of it
Project
none
Learning
Objectives
Topics
Week
16
Final Exam
Class Work
(Methods)
Materials
(Required
Readings)
Assignments
Ⅷ. Special Accommodations
If you need academic accommodations for a disability, you should make an
appointment to talk with me as soon as possible. Additional supports, such as advanced seat
assignment, lecture note, tutor, flexible due date of homework and exam date, will be
provided if it is necessary
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