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4KG3-Winter 2017 1 of 5
4KG3
Data Mining and Business Intelligence
Winter 2017 Course Outline
Information Systems Area
DeGroote School of Business
McMaster University
COURSE OBJECTIVE
Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable
information to help corporate executives, business managers and other end users make more informed
business decisions. Students will learn the concepts, techniques, and applications of data mining for
business intelligence through lectures, class discussions, hands-on assignments, and term paper
presentations. Data mining and business intelligence is a very important topic in information systems area
as well as in other areas such as finance, marketing, supply chain management, healthcare etc. It will help
students to advance in their future career.
INSTRUCTOR AND CONTACT INFORMATION
To be arranged
TA
@mcmaster.ca
Office: DSB A211
Office Hours:
To be arranged
Tel: (905) 525-9140 x
Dr. Yufei Yuan
Instructor
[email protected]
Office: DSB A204
Office Hours:
To be arranged
Tel: (905) 525-9140 x23982
Class Time: Tuesdays 2:30-5:20 pm
Classroom: DSB B106
Tutorial and lab: Tuesdays 5:30 -6:30 pm, DSB B106
Course Website: http://avenue.mcmaster.ca
COURSE ELEMENTS
Credit Value: 3
Avenue: Yes
Participation: Yes
Team skills: Yes
Verbal skills: Yes
Written skills: Yes
IT skills: Yes
Numeracy: No
Innovation: Yes
Global: Yes
Political: No
Social: No
COURSE DESCRIPTION
This advanced commerce course introduces basic data mining technologies and their use for business
intelligence. Students will learn how to analyze the business needs for knowledge discovery in order to
create competitive advantages and how to apply data mining technologies appropriately in order to realize
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4KG3-Winter 2017 2 of 5
their real business value. Students will gain hands-on experience through assignments and learn from real
world practice through seminar presentation.
The course will cover the following topics:
•
•
•
•
•
The need for business intelligence
Data mining concepts, methods, and process
Data mining technologies
Data mining applications
Data mining case studies
LEARNING OUTCOMES
Upon completion of this course, students will be able to complete the following key tasks:
 Understand the basic concept of business intelligence
 Understand the basic concept and the process of data mining
 Learn basic data mining technologies
 Learn how to use business intelligence to solve business problems
REQUIRED COURSE MATERIALS AND READINGS
[Shmueli] Galit Shmueli, Nitin Patel, and Peter Bruce, Data Mining for Business Intelligence:
Concepts, Technologies, and Applications in Microsoft Office Excel with XLminer, 2nd edition,
John Wiley & Sons, Inc. 2010, (ISBN 978-0-470-52682-8)
OPTIONAL COURSE MATERIALS AND READINGS
Reference Textbook (Reserved in INNIS Library)
[Turban] Efraim Turban, Ramesh Sharda, Dursun Delen, David King, Business Intelligence: A
Managerial Approach, 2nd edition, Pearson Prentice Hall, 2010.
[Liebowitz] Jay Liebowitz (Editor), Big Data and Business Analytics, CRC Press, 2013
http://books.google.ca/books?hl=en&lr=&id=Oq9znTz7FGoC&oi=fnd&pg=PP1&dq=big+data+a
nd+business+analytics&ots=JRYMAILyBs&sig=puOmoh51sJzGsdMIRU1u2HVvJI#v=onepage&q=big%20data%20and%20business%20analytics&f
=true
Reference Material on the Web
[W1] The resource for business intelligence http://www.businessintelligence.com/
[W2] SAS Business Intelligence http://www.sas.com/technologies/bi/
[W3] Microsoft Business Intelligence http://www.microsoft.com/bi/
[W4] Data warehouse information centre http://www.dwinfocenter.org/
[W5] Guide to Data Mining http://www.data-mining-guide.net/
[W6] BI service Adastra Canada http://www.adastracorp.com/
[W7] Dataset for data mining http://www.kdnuggets.com/datasets/
[W8] Data Mining Book http://www.dataminingbook.com/
[W9] Conferences / Workshops http://www.kmining.com/info_conferences.html
[W10] XLMiner online help http://www.resample.com/xlminer/help/Index.htm
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4KG3-Winter 2017 3 of 5
EVALUATION
Learning in this course results primarily from reading, in-class discussion, assignments, term papers, and
exams. Final exam is in the form of multiple choices and sort-answer questions. Your final grade will be
calculated as follows:
Components and Weights
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Student Seminar
Final Exam
Total
Data warehouse modeling
Decision tree analysis
Association analysis
Neural Networks
Business intelligence applications
10%
10%
10%
10%
10%
Cover materials for the entire course
50%
100%
CLASS SCHEDULE (SUBJECT TO POSSIBLE MODIFICATION)
Week
Date
Topic
Readings/Assignments
1
Jan. 10
Introduction to business intelligence
[Turban] Ch.1
2
Jan. 17
Data warehousing
[Turban] Ch. 2
3
Jan. 24
Business Analytics and data
visualization
[Turban] Ch. 3
[Shmueli] Ch. 3
Assignment 1 due
4
Jan. 31
Business driven data mining
objectives and process
[Shmueli] Ch. 1, 2
[Groth] Ch. 1, 2, 3
[Turban] Ch. 4, 5
Student seminar proposal due
5
Feb. 7
Data exploration and dimension
reduction, Evaluating classification
and prediction performance
[Shmueli] Ch. 3, 4, 5
Student seminar 1
6
Feb. 14
Simple classification methods
[Shmueli] Ch. 7, 8
Feb. 20 - 26
Mid-term Recess
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7
Feb. 28
Classification Decision tree and
Discriminant analysis
[Shmueli] Ch. 9, 12
Assignment 2 due
8
Mar. 7
Association rules and clustering
analysis
[Shmueli] Ch. 13, 14
Student seminar 2
9
Mar. 14
Prediction algorithms: Multiple
linear regression and Logistic
regression
[Shmueli] Ch. 6, 10
Assignment 3 due
10
Mar. 21
Neural Networks
[Shmueli] Ch. 11
Student seminar 3
11
Mar. 28
Business performance management
[Turban] Ch. 3, 6
Assignment 4 due
12
April 4
Guest speech (to be arranged)
Student seminar 4
Student Seminar Guidelines
Objective:
To present a research topic that investigate the application, the new trend, and the issues associated with
data mining and business intelligence. Students are expected to work as a team with up to 3 students.
Topic Selection:
The topic of your research may be on any contemporary issue relating to data resource management
technology and business applications. Following are suggested but not limited topics:
1. Issues and challenges of big data and BI
2. Review business intelligence applications in a special field
3. Business intelligence case study
4. Advances of data mining technologies
5. Security and privacy issues of big data collection and data mining
6. Data mining application success factors
7. New trends of big data and business intelligence
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4KG3-Winter 2017 5 of 5
Tasks:
1. Students are expected to form a team with up to 3 students and submit a proposal for the topic
presentation. The proposal should include the team member name, title, brief description of the
topic under investigation, and preference for the date of presentation within four seminar slots
indicated in class schedule.
2. Student teams will be assigned to present their topic in one of the four student seminar slots.
3. Students may search and collect relevant information from news and articles from variety of
sources but mainly from internet. You need to have clear focus and to organize your presentation
in a meaningful way.
4. Students will make a PowerPoint presentation with about 10-15 slides. And make 10 minutes
presentation. The slides should be submitted to the drop box the day before presentation.
5. Student presentation will be evaluated by classmates and the instructor based on the value of the
content and presentation logic and skill.
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