Download syllabus - Stevens Institute of Technology

MIS-636 - A
Data Warehousing and Business Intelligence Course Syllabus Course Info: Wednesday, 6:15-8:45pm, BC-310
Contact Information
Professor: Joseph Morabito, Ph.D.
Office: Babbio 419
Office Hours: By Appt. Phone: 201-216-5304 Email: jmorabit@stevens.edu
II. Required Course Materials 1. The Data Warehouse Lifecycle Toolkit: Practical Techniques for
Building Data Warehouse and Business Intelligence Systems. Second
Edition. Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., and Becker,
B. John Wiley & Sons, 2008. ISBN 978-0-470-14977-5.
2. "Enterprise Intelligence: A Case Study and the Future of Business
Intelligence" Morabito, J., Stohr, E., Genc, Y. International Journal of Business
Intelligence Research. 2011. 3. Case studies and papers in addition to the above
4. "DW packets" of design and management templates Suggested Readings 1. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
Kimball, R. and Ross, M. Second Edition. John Wiley & Sons, 2006. 2. The Data Warehouse ETL Toolkit: Practical Techniques for Extracting,
Cleaning, Conforming, and Delivering Data. Kimball, R., and Caserta, J.
John Wiley & Sons, 2004.
Supplementary Readings, Exercises, and Assignments:
All other readings, exercises, and assignments are posted to our electronic
course site. III. Course Objectives and Learning Goals
This course will focus on the design and management of data warehouse (DW)
and business intelligence (BI) systems. The DW is the central element in
collecting, integrating, and making sense - knowledge discovery - of an
organization's data. BI concerns the full range of analytical applications
and its delivery to the desktop of users. Each of these areas is
fundamentally different in character - business, architectural, and
technical - from traditional databases and applications. Together they form
the basis of modern business analytics and decision making in organizations
today. Course Outcomes
The following outcomes include both the conceptual, design, and operational
perspectives of the following:
1. Understand the role of data and analytics in the competitiveness of
organizations
2. Locate and integrate data
3. Data Design (Star-schema, Surrogate Keys, ODS)
4. Real-time Partitioned Tablespaces, Aggregations
5. MDDB (Cube Design) & OLAP
6. Enterprise planning and Conformed Dimensions
7. Track History
8. Advanced Modeling (Snowflaking, Outrigger, and Bridge Guidelines)
9. Designing and Managing Very Large Rapidly Changing Dimensions
10. Implementation (ETL, Data Staging, and Physical Design)
11. Data Visualization
12. Understanding Big Data
13. The Value Chain and BI Application Development
14. Manage a full scale DW/BI project Data design skills: Additional learning objectives include the assessment
of a business or application domain and the design of a corresponding multi-
dimensional database. Emphasis is placed on developing advanced design
techniques. Team skills: The final project is a team presentation of an end-to-end
business intelligence system, from source systems through database design
to data visualization formats for end users. IV. Assignments There are many team exercises, an individual mid-term exam, and a final
team project. There will be an individual assignment distributed in class. The exam will
cover the first half of the course. There will be a final team assignment due at the last meeting. The
assignment will include the design and construction of a full data
warehouse and OLAP application, including an OLAP cube, loading schedule,
reports, and OLAP navigation applications. This will be accomplished with a
commercial product. The course is organized around the following themes:
1. Analytics & Competitive Advantage
2. Case Studies & Literature Review
3. Maturity Models for DW and BI
4. BI and the Value Chain
5. Locating and integrating data
6. Project Management & Requirements
7. Architecture & Tool Selection
8. Data Design - Star schema, ODS, real-time component, MDDB (cube)
9. Implementation: ETL, Data Staging, and Physical Design
10. BI Application Development (includes OLAP, Portal, and Dashboard
Design)
11. Data Visualization
12. Big Data
13. Deployment & Growth Grading The grading of the assignments and their weights are as follows:
1. Mid-term (Individual Assignment) 30%
2. Final Presentation (Team Assignment) 40%
3. Accreditation Assignment (Individual) 10%
3. Class Participation, Exercises, and Homework (Team) 20% V. Academic Honesty Policy
Ethical Conduct
The following statement is printed in the Stevens Graduate Catalog and
applies to all students taking Stevens courses, on and off campus. "Cheating during in-class tests or take-home examinations or homework is,
of course, illegal and immoral. A Graduate Academic Evaluation Board
exists to investigate academic improprieties, conduct hearings, and
determine any necessary actions. The term 'academic impropriety' is meant
to include, but is not limited to, cheating on homework, during in-class or
take home examinations and plagiarism." Consequences of academic impropriety are severe, ranging from receiving an
"F" in a course, to a warning from the Dean of the Graduate School, which
becomes a part of the permanent student record, to expulsion. Reference: The Graduate Student Handbook, Stevens Institute of
Technology. Consistent with the above statements, all homework exercises, tests and
exams that are designated as individual assignments must contain the
following signed statement before they can be accepted for grading.
____________________________________________________________________
I pledge on my honor that I have not given or received any unauthorized
assistance on this assignment/examination. I further pledge that I have not
copied any material from a book, article, the Internet or any other source
except where I have expressly cited the source.
Name (Print) ___________________ Signature ________________ Date:
_____________ Please note that assignments in this class may be submitted to
www.turnitin.com, a web-based anti-plagiarism system, for an evaluation of
their originality. Grading Scale |Grade|Score |Grade|Score |
|A |93-100 |C |73-76 |
|A- |90-92 |C- |70-72 |
|B+ |87-89 |F |