Showker 208 MWF 9:05AM - 9:55AM
Professor Michel Mitri
234 ZSH Office Hours: TTh 9:30AM - 12:00PM
568-3019 or by appointment
mitrimx@jmu.edu http://cob.jmu.edu/mitrimx
Course Goals:
This course provides a comprehensive discussion of and practical experience in advanced database techniques, data visualization, data warehousing, online analytical processing (OLAP), data mining, decision support systems (DSS), artificial intelligence (AI) methods and other Business Intelligence (BI) topics. Students gain practical experience using contemporary BI tools and technologies, and apply sound design principles for creating intelligent solutions to realistic business problems.
CIS 463 is an advanced and relatively technical elective. Students taking.CIS 463 are expected to have successfully completed CIS 330, and in particular must be comfortable with relational database technology and SQL queries including joins, aggregate groupings, and subqueries. In addition, students are expected to have completed at least an introductory level programming class, such as CIS 221.
Learning Outcomes:
1. The student will provide a working definition for business intelligence in general and various classifications of business intelligence.
2. The student will build upon and enhance his/her knowledge of relational database technology and skills in performing complex SQL database queries, procedural SQL coding, and stored procedures/functions.
3. The student will demonstrate knowledge of XML data structures, and proficiency in querying XML data sources using XPath and XQuery.
4. The student will become familiar with a variety of data visualization options, including bar/line/pie/bubble/org/treemap charts, digital dashboards, and key performance indicator gauges, and with how to map SQL queries to their appropriate visualizations.
5. The student will demonstrate understanding of business performance management systems and methodologies, and apply a balanced scorecard approach toward development of digital dashboards.
6. The student will demonstrate knowledge and practical proficiency with the ETL process, using it to extract operational data, transform and cleanse this data, and load it into a data warehouse or data mart.
7. The student will demonstrate a working knowledge of the difference in structure between relational databases and multidimensional data warehouse architectures, show an understanding of the relationship between facts tables and dimension tables, as well as an understanding basic star and snowflake schemas.
8. The student will design online analytical processing (OLAP) models, and build multidimensional cubes that are capable of providing summary information as well as drilling down for detailed data.
9. The student will demonstrate an understanding of of a variety of data mining models and structures: inductive decision trees, naïve Bayes algorithms, clustering algorithms, neural networks, and time sequences, and identify the appropriate types of models for various categories of business-related problems.
10. The student will demonstrate proficiency in applying data mining models to simple data sets.
11. The student will use commercial and/or open-source business intelligence tools to develop their BI applications.
12. The student will demonstrate proficiency applying BI techniques to a live enterprise data repository representing a realistic business environment.
13. The student will demonstrate an understanding of how BI practices and tools support and impact decision making, planning, and business processes within an organization.
14. The student will use demonstrate in-depth knowledge of a selected BI topic by writing a term paper.
Grading:
15% in-class participation. I will keep a tally of your attendance. Many classroom activities involve doing exercises or tutorials. You get credit for being here working on the tutorials or exercises.
15% Midterm exam.
30% Homework assignments (including in-class lab exercises)
20% Term paper
20% Final exam
Class Policy:
· Late policy: Each homework assignment is graded on a 0-10 basis (best score = 10). Each day late costs you 1 point. There is NO makeup for missing a class (even if you are sick).
· Exam grades must average at least 60% in order to pass this course.
· Assignment grades must average at least 60% in order to pass this course.
· Exceptions in the form of either WP or WF may be granted if a student demonstrates the existence of extenuating circumstances, outside the student's control, that prevented attending of performing well in class. A student seeking an exception to this rule must provide a letter to the Director of the Program teaching the class describing the request and the justification (including supporting documentation).
· In case of inclement weather, see JMU's cancellation policy http://www.jmu.edu/JMUpolicy/1309.shtml). Include additional information specific to the class or to your commuting situation.
· If you are a student with a documented disability who will be requesting accommodations in my class, please make sure you are registered with the Office of Disability Services, Wilson Hall, Room 107, 568-6705 and provide me with an Access Plan letter outlining your accommodations. I will be glad to meet with you privately during my office hours to discuss your special needs. Students who have an approved “Access Plan” from the JMU Office of Disability Services which calls for either (1) a quiet testing environment or (2) extended time on tests and examinations must present a request for testing accommodation to their instructor at least seven (7) calendar days prior to the scheduled beginning time of the test or examination. The reason for this advance notice is to provide the instructor ample time to arrange for suitable facilities. Seven days notice is required in recognition that the College of Business lacks readily available classroom and conference room space.
· I will NOT be automatically dropping students due to attendance, either in the first week or anytime during the semester. If you choose to drop the course, you must do so yourself.
· Academic integrity - Cheating will not be tolerated and will be dealt with in a manner consistent with the guidelines of the JMU Honor System. Working with each other on homework and lab assignments is encouraged and expected in this course, as long as it leads to independence and greater learning. However, direct copy of any material submitted for grading is a violation of the JMU Honor Code and will be treated as such. Also, over-reliance on the help of another student to the point of dependency is unhealthy and goes against the spirit of the honor code. Your signature on the exams and your emailed assignments constitute your pledge to the James Madison University Honor Code.
Sources of Information:
· Text Business Intelligence: A Managerial Approach 2/e by E. Turban, R. Sharda, D. Delen, D. King.
· In-class handouts and exercises
· Extra Powerpoint slides and documents available from my web site
· Links to other reading assignments (e.g., Microsoft tutorials)
TENTATIVE CLASS AND ASSIGNMENT SCHEDULE
(subject to change if needed)
|
Date |
Topic |
In-Class Activities |
Textbook Readings |
Assignment Due |
|
Week 1 1/9 1/11 1/13 |
Intro to BI
Review of relational database and SQL
Advanced SQL queries
|
Getting familiar with SQL Server, Microsoft AdventureWorks database, and Microsoft SQL syntax |
Chapter 1 |
|
|
Week 2
1/16 (MLK) 1/18 1/20 |
Advanced SQL queries
T-SQL and Stored Procedures
|
Lots of querying on the Adventureworks database, and exploring the AdventureworksDW database.
|
|
|
|
Week 3 1/23 1/25 1/27
|
T-SQL and Stored Procedures |
Lots of stored procedure and trigger work in T-SQL on Adventureworks and AdventureworksDW databases.
|
Chapter 6 |
Assignment #1: Advanced SQL queries
Chapter 1 Readings |
|
Week 4 1/30 2/1 2/3 |
XML, XPath, and XQuery | Term paper proposal due | ||
|
Week 5
2/6 2/8 2/10 |
XML, XPath, and XQuery |
|
||
|
Week 6
2/13 2/15 2/17 |
Data Visualization
GoogleCharts
Drill-down dashboards
|
Working with GoogleCharts. Working with my Dashboard generator.
|
Chapter 3 |
Assignment #2: TriggeT-SQL, XML, and Stored Procedures
Chapter 6 Readings |
|
Week 7
2/20 2/22 2/24 |
Data Visualization
Google Data Visualizations
|
Working with Google Data Visualizations
|
||
|
Week 8
2/27 2/29 3/2 |
Data Warehousing (ETL)
Midterm Exam |
Microsoft SSIS Tutorial |
Chapter 2 |
AssignAssignment #3: Data Visualization exercises and project
Ch 3 and other data visualization Readings |
|
3/5 3/7 3/9 |
Spring Break |
|||
|
Week 9 3/12 3/14 3/16
|
Data Warehousing (ETL) |
|
|
|
|
Week 10
3/19 3/21 3/23 |
Data Warehousing (ETL) |
|
Assignment #4: Data WData Warehousing exercises and project
Chapter 2 and other DW and OLAP Readings |
|
|
Week 11
3/26 3/28 3/30
|
OLAP |
OLAP Tutorials |
Chapter 4 |
|
|
Week 12
4/2 4/4 4/6 |
OLAP |
OLAP Tutorials
|
Term paper due | |
|
Week 13
4/9 4/11 4/13
|
OLAP |
In-class problem-solving using OLAP |
|
|
|
Week 14
4/16 4/18 4/20
|
Data Mining |
Microsoft data mining tutorials
|
Chapter 5 |
Assignment #5 OLAP exercises, project and readings
|
|
Week 15
4/23 4/25 4/27
|
Data Mining and Text/Web mining |
Data mining problem solving |
|
|
|
Finals Week
|
Final ExamMon. 4Mon. 4/30/12 8-10AM |
|
Assignment #6: Data Mining Exercises and readings Ch 5 |