Description
Carl H. Lindner College of Business University of Cincinnati Business Intelligence
Roger Chiang Page 1 of 3 IS4030 (Spring 2014)
Carl H. Lindner College of Business
University of Cincinnati
22-IS-4030 Business Intelligence Spring 2014
January 6 – April 24, 2014
Tuesday and Thursday: 3:30 – 4:50pm
Instructor: Roger H.L. Chiang
Office: 514 Lindner Hall
Email: [email protected] (Please include ‘IS4030’ in the subject line of your emails)
Phone: 5567086
Office Hours: Tuesday & Thursday, 2:30-3:30pm, and by appointment
Email is the best way to contact me. I should reply almost all your emails within 24 hours.
Therefore, I expect the same courtesy from you.
Course Content and Objectives:
The course introduces emerging business intelligence technologies such as data warehousing, on-
line analytic processing (OLAP), data mining and text mining. Data warehouses have been created
to store (archive) data from operational information systems so that it could be easily accessed. In
the last 10 years, this new information technology has matured and found to be very useful in
generating valuable control and decision-support business intelligence for many organizations in
adjusting to their competitive business environment. As a result, there is now a fairly stable body of
knowledge about the design, development and operation of data warehouses, which you will learn
in this course. OLAP, data mining and text mining are common techniques in generating business
intelligence from data warehouses and user-generated contents available in social media. The
objectives of this class include:
? Introduce the business intelligence applications for business competition
? Understand data warehouse concepts and building blocks
? Conduct dimensioning modeling in building data warehouses
? Learn the current applications and trends of data warehouses
? Learn BI techniques such as OLAP, data mining, text mining and web mining
Pre-requisite: IS3030 (If you don't meet the prerequisite, please contact instructor immediately.)
Course Administration:
You are expected to come prepared for every class period by reading the assigned reading material
posted in the Blackboard. Class lectures will discuss extensively on the key concepts and ideas of
each topic with additional insight. Most importantly, the lectures will clarify students’ questions and
initiate the lively class discussion. The course involves lectures, homework assignments, 2 quizzes,
midterm exam, final exam and a term project. Attendance and class participation are critical to
learning in this course; so, your attendance for scheduled classes is mandatory. If your personal
schedule entails missing some class sessions (more than 3), please drop this course. Absence for a
class does not relieve you of your responsibility for the subject matter, assignments when they are
due, and other course-related issues discussed during that class period.
Students are expected to take the exams and quizzes as scheduled. Make-up exams won’t be given
unless arrangements are made prior to the regularly scheduled exam and that too under extenuating
circumstances only.
Requests for extra work to improve course grade will not be entertained.
Roger Chiang Page 2 of 3 IS4030 (Spring 2014)
22-IS-4030 Business Intelligence Spring 2014
Week Date Topics Readings
1 1/7 (T) Business Intelligence & Analytics (BI&A) Introduction Loshin: Chapter 1
1 1/9 (Th) From Big Data to Big Analytics to Big Impacts Handout
2 1/14 (T) Business Intelligence & Analytics Technologies:
Data Warehousing, Data Mining and Text Analytics
Case Study and
Reading Articles
2 1/16 (Th) Evolution of Data Management Handout
3 1/21 (T) Types of Database Systems Handout
3 1/23 (Th) Introduction to Data Warehouses Hoffer et al.: Chapter 9
4 1/28 (T) Data Warehousing: Concepts, Building Blocks & Process Hoffer et al.: Chapter 9
4 1/30 (Th) Data Warehousing: Concepts, Building Blocks & Process Hoffer et al.: Chapter 9
5 2/4 (T) Dimensional Modeling for Data Warehouse Design Ponniah: Chapter 10
5 2/6 (Th) Dimensional Modeling for Data Warehouse Design
Dimensional Modeling Case Study
Ponniah: Chapter 10
6 2/11 (T) Dimensional Modeling for Data Warehouse Design
Dimensional Modeling Using ERWin
Handout
6 2/13 (Th) Quiz I
Advanced Dimensional Modeling: Dimensional Tables
Ponniah: Chapter 11
7 2/18 (T) Advanced Dimensional Modeling: Dimensional Tables Ponniah: Chapter 11
7 2/20 (Th) Advanced Dimensional Modeling: Fact Tables Ponniah: Chapter 11
8 2/25 (T) Advanced Dimensional Modeling: Fact Tables Ponniah: Chapter 11
8 2/27 (Th) Advanced Dimensional Modeling Case Study Modeling Case
9 3/4 (T) Midterm Examination
9 3/6 (Th) Information Delivery & Online Analytics Processing
(OLAP)
Ponniah: Chapters 14 &
15
10 3/11 (T) Introduction to Data Mining Han et al.: Chapter 1
10 3/13 (Th) Introduction to IBM SPSS Modeler for Data/Text mining Handout: Manual
11 Spring Break
12 3/25 (T) Data Mining: Classification Han et al.: Chapter 6
12 3/27 (Th) Data Mining: Decision Tree Han et al.: Chapter 8
13 4/1 (T) Data Mining: Clustering Han et al.: Chapter 10
13 4/3 (Th) Business Applications of Data/Text Mining Handout
14 4/8 (T) Quiz II
Data Visualization
Handout
14 4/10 (Th) Text Mining and Sentiment Analysis Handout
15 4/15 (T) Project Presentation
15 4/17 (Th) Project Presentation
16 4/24 (Th) Final Exam: 2:15-4:15pm
Exams, Quizzes and Homework Assignments:
There are two 40-minute quizzes, one 80-minute midterm and a two-hour final examination. The
quizzes and exams contain both objective (e.g., T/F, fill-in, multiple choices) and essay questions.
Students are expected to take the quiz and exams as scheduled. Make-up exam/quiz won’t
be given unless arrangements are made prior to the regularly scheduled exam/quiz. Homework
assignments will be due on the date specified. Late submissions will not be accepted.
Term Project:
There is a group term project and each group can have up to 3 students. Each team can work on a
data warehousing, data mining, or BI application project. The detailed instructions of the term
project, the project report and its class presentation will be distributed as a separate handout in class.
Peer evaluation will be administered to assess each team member’s contribution to the project.
Roger Chiang Page 3 of 3 IS4030 (Spring 2014)
Course Resources:
This course doesn’t have a required text book. A course pack has been developed for you. Lecture
notes and reading material will be available from Blackboard. I would like to provide a list of BI
education resources:
1. The Data Warehousing Institute (TDWI): www.dw-institute.com/
2. IBM Academic Initiates: www.ibm.com/developerworks/university/academicinitiative/
3. Gartner Research: www.uc.edu/ucit/gartner/
4. KDnuggets: www.kdnuggets.com/
5. Microsoft Dynamics Academic Alliance: www.microsoft.com/dynamicsaa
6. ORACLE Academy:https://oai.oracle.com/oaiapps/index1.html
7. SAP University Alliances: www.sdn.sap.com/irj/uac/index
8. SAS Global Academic Program: www.sas.com/academic
9. Teradata Student Network: www.teradatastudentnetwork.com/ (I will provide the password.)
10. Walton College of Business at University of Arkansas: enterprise.waltoncollege.uark.edu/
Reference Books:
Business Intelligence
1. David Loshin, “Business Intelligence: The Savvy Manager’s Guide”, Morgan Kaufmann, 2003
2. Galit Shmueli, Nitin R. Patel and Peter C. Bruce, “Data Mining for Business Intelligence”, Second
Edition, Wiley, 2010
Data Mining
1. J iawei Han, Micheline Kamber and J ian Pei, “Data Mining: Concepts and Techniques”, 3
rd
edition, Morgan Kaufmann, 2011
2. Gordon S. Linoff & Michael J .A. Berry, “Data Mining Techniques: For Marketing, Sales, and
Customer Relationship Management”, 3
rd
edition, Wiley, 2011
Data Warehousing
1. William H. Inmon, “Building the Data Warehouse”, 4
th
edition, Wiley, 2005
2. Ralph Kimball & Margy Ross, “The Data Warehouse Toolkit: The Complete Guide to
Dimensional Modeling”, 2
nd
edition, Wiley, 2002
3. Ralph Kimball et al., “The Data Warehouse Lifecycle Toolkit”, 2
nd
edition, Wiley, 2008
4. Paulraj Ponniah, “Data Warehousing Fundamentals for IT Professionals”, Wiley, 2
nd
Edition,
2010
Database Systems
1. J effrey A. Hoffer et al., “Modern Database Management”, 11
th
edition, Prentice Hall, 2013
Course Grade: Your final course grade will be determined as:
2 Exams 50% (Mid Exam =20%; Final Exam =30%)
2 Quizzes 20%
6 Homework Assignments 15%
Term Project 10%
Class Participation 5%
100%
Note: A single instance of cheating or plagiarism is all it takes for immediate dismissal of the
concerned parties from the course with a ‘F’ grade and a report of the incidence to Director of
Undergraduate Program.
doc_672244024.pdf
Carl H. Lindner College of Business University of Cincinnati Business Intelligence
Roger Chiang Page 1 of 3 IS4030 (Spring 2014)
Carl H. Lindner College of Business
University of Cincinnati
22-IS-4030 Business Intelligence Spring 2014
January 6 – April 24, 2014
Tuesday and Thursday: 3:30 – 4:50pm
Instructor: Roger H.L. Chiang
Office: 514 Lindner Hall
Email: [email protected] (Please include ‘IS4030’ in the subject line of your emails)
Phone: 5567086
Office Hours: Tuesday & Thursday, 2:30-3:30pm, and by appointment
Email is the best way to contact me. I should reply almost all your emails within 24 hours.
Therefore, I expect the same courtesy from you.
Course Content and Objectives:
The course introduces emerging business intelligence technologies such as data warehousing, on-
line analytic processing (OLAP), data mining and text mining. Data warehouses have been created
to store (archive) data from operational information systems so that it could be easily accessed. In
the last 10 years, this new information technology has matured and found to be very useful in
generating valuable control and decision-support business intelligence for many organizations in
adjusting to their competitive business environment. As a result, there is now a fairly stable body of
knowledge about the design, development and operation of data warehouses, which you will learn
in this course. OLAP, data mining and text mining are common techniques in generating business
intelligence from data warehouses and user-generated contents available in social media. The
objectives of this class include:
? Introduce the business intelligence applications for business competition
? Understand data warehouse concepts and building blocks
? Conduct dimensioning modeling in building data warehouses
? Learn the current applications and trends of data warehouses
? Learn BI techniques such as OLAP, data mining, text mining and web mining
Pre-requisite: IS3030 (If you don't meet the prerequisite, please contact instructor immediately.)
Course Administration:
You are expected to come prepared for every class period by reading the assigned reading material
posted in the Blackboard. Class lectures will discuss extensively on the key concepts and ideas of
each topic with additional insight. Most importantly, the lectures will clarify students’ questions and
initiate the lively class discussion. The course involves lectures, homework assignments, 2 quizzes,
midterm exam, final exam and a term project. Attendance and class participation are critical to
learning in this course; so, your attendance for scheduled classes is mandatory. If your personal
schedule entails missing some class sessions (more than 3), please drop this course. Absence for a
class does not relieve you of your responsibility for the subject matter, assignments when they are
due, and other course-related issues discussed during that class period.
Students are expected to take the exams and quizzes as scheduled. Make-up exams won’t be given
unless arrangements are made prior to the regularly scheduled exam and that too under extenuating
circumstances only.
Requests for extra work to improve course grade will not be entertained.
Roger Chiang Page 2 of 3 IS4030 (Spring 2014)
22-IS-4030 Business Intelligence Spring 2014
Week Date Topics Readings
1 1/7 (T) Business Intelligence & Analytics (BI&A) Introduction Loshin: Chapter 1
1 1/9 (Th) From Big Data to Big Analytics to Big Impacts Handout
2 1/14 (T) Business Intelligence & Analytics Technologies:
Data Warehousing, Data Mining and Text Analytics
Case Study and
Reading Articles
2 1/16 (Th) Evolution of Data Management Handout
3 1/21 (T) Types of Database Systems Handout
3 1/23 (Th) Introduction to Data Warehouses Hoffer et al.: Chapter 9
4 1/28 (T) Data Warehousing: Concepts, Building Blocks & Process Hoffer et al.: Chapter 9
4 1/30 (Th) Data Warehousing: Concepts, Building Blocks & Process Hoffer et al.: Chapter 9
5 2/4 (T) Dimensional Modeling for Data Warehouse Design Ponniah: Chapter 10
5 2/6 (Th) Dimensional Modeling for Data Warehouse Design
Dimensional Modeling Case Study
Ponniah: Chapter 10
6 2/11 (T) Dimensional Modeling for Data Warehouse Design
Dimensional Modeling Using ERWin
Handout
6 2/13 (Th) Quiz I
Advanced Dimensional Modeling: Dimensional Tables
Ponniah: Chapter 11
7 2/18 (T) Advanced Dimensional Modeling: Dimensional Tables Ponniah: Chapter 11
7 2/20 (Th) Advanced Dimensional Modeling: Fact Tables Ponniah: Chapter 11
8 2/25 (T) Advanced Dimensional Modeling: Fact Tables Ponniah: Chapter 11
8 2/27 (Th) Advanced Dimensional Modeling Case Study Modeling Case
9 3/4 (T) Midterm Examination
9 3/6 (Th) Information Delivery & Online Analytics Processing
(OLAP)
Ponniah: Chapters 14 &
15
10 3/11 (T) Introduction to Data Mining Han et al.: Chapter 1
10 3/13 (Th) Introduction to IBM SPSS Modeler for Data/Text mining Handout: Manual
11 Spring Break
12 3/25 (T) Data Mining: Classification Han et al.: Chapter 6
12 3/27 (Th) Data Mining: Decision Tree Han et al.: Chapter 8
13 4/1 (T) Data Mining: Clustering Han et al.: Chapter 10
13 4/3 (Th) Business Applications of Data/Text Mining Handout
14 4/8 (T) Quiz II
Data Visualization
Handout
14 4/10 (Th) Text Mining and Sentiment Analysis Handout
15 4/15 (T) Project Presentation
15 4/17 (Th) Project Presentation
16 4/24 (Th) Final Exam: 2:15-4:15pm
Exams, Quizzes and Homework Assignments:
There are two 40-minute quizzes, one 80-minute midterm and a two-hour final examination. The
quizzes and exams contain both objective (e.g., T/F, fill-in, multiple choices) and essay questions.
Students are expected to take the quiz and exams as scheduled. Make-up exam/quiz won’t
be given unless arrangements are made prior to the regularly scheduled exam/quiz. Homework
assignments will be due on the date specified. Late submissions will not be accepted.
Term Project:
There is a group term project and each group can have up to 3 students. Each team can work on a
data warehousing, data mining, or BI application project. The detailed instructions of the term
project, the project report and its class presentation will be distributed as a separate handout in class.
Peer evaluation will be administered to assess each team member’s contribution to the project.
Roger Chiang Page 3 of 3 IS4030 (Spring 2014)
Course Resources:
This course doesn’t have a required text book. A course pack has been developed for you. Lecture
notes and reading material will be available from Blackboard. I would like to provide a list of BI
education resources:
1. The Data Warehousing Institute (TDWI): www.dw-institute.com/
2. IBM Academic Initiates: www.ibm.com/developerworks/university/academicinitiative/
3. Gartner Research: www.uc.edu/ucit/gartner/
4. KDnuggets: www.kdnuggets.com/
5. Microsoft Dynamics Academic Alliance: www.microsoft.com/dynamicsaa
6. ORACLE Academy:https://oai.oracle.com/oaiapps/index1.html
7. SAP University Alliances: www.sdn.sap.com/irj/uac/index
8. SAS Global Academic Program: www.sas.com/academic
9. Teradata Student Network: www.teradatastudentnetwork.com/ (I will provide the password.)
10. Walton College of Business at University of Arkansas: enterprise.waltoncollege.uark.edu/
Reference Books:
Business Intelligence
1. David Loshin, “Business Intelligence: The Savvy Manager’s Guide”, Morgan Kaufmann, 2003
2. Galit Shmueli, Nitin R. Patel and Peter C. Bruce, “Data Mining for Business Intelligence”, Second
Edition, Wiley, 2010
Data Mining
1. J iawei Han, Micheline Kamber and J ian Pei, “Data Mining: Concepts and Techniques”, 3
rd
edition, Morgan Kaufmann, 2011
2. Gordon S. Linoff & Michael J .A. Berry, “Data Mining Techniques: For Marketing, Sales, and
Customer Relationship Management”, 3
rd
edition, Wiley, 2011
Data Warehousing
1. William H. Inmon, “Building the Data Warehouse”, 4
th
edition, Wiley, 2005
2. Ralph Kimball & Margy Ross, “The Data Warehouse Toolkit: The Complete Guide to
Dimensional Modeling”, 2
nd
edition, Wiley, 2002
3. Ralph Kimball et al., “The Data Warehouse Lifecycle Toolkit”, 2
nd
edition, Wiley, 2008
4. Paulraj Ponniah, “Data Warehousing Fundamentals for IT Professionals”, Wiley, 2
nd
Edition,
2010
Database Systems
1. J effrey A. Hoffer et al., “Modern Database Management”, 11
th
edition, Prentice Hall, 2013
Course Grade: Your final course grade will be determined as:
2 Exams 50% (Mid Exam =20%; Final Exam =30%)
2 Quizzes 20%
6 Homework Assignments 15%
Term Project 10%
Class Participation 5%
100%
Note: A single instance of cheating or plagiarism is all it takes for immediate dismissal of the
concerned parties from the course with a ‘F’ grade and a report of the incidence to Director of
Undergraduate Program.
doc_672244024.pdf