||Data Warehousing for Analytics
||This advanced course will provide students with an in-depth understanding of the design and implementation of database warehousing and analytics database systems. Specific topics include data warehouse modeling and architecture, the ETL process, administration, security, column-store, streaming and NoSQL databases, and complex event processing. Students develop a complete data warehouse system including implementation of a business intelligence suite.
||Upon successful completion of this course, students will be able to:
- Translate business needs and drivers into IT requirements for business intelligence systems.
- Use the supporting technologies and data models for business intelligence including the process of and techniques for transforming business transaction data into appropriate analytic structures
- Explore state-of-the-art solutions for building and managing large data warehouse systems
- Discuss appropriate modeling approaches for a variety of industry specific requirements such as healthcare, banking, insurance, on-line advertising, and others.
- Develop a complete business intelligence system in a team setting using all of the tools and techniques presented during the course.
Upon successful completion of this course, students will have advanced skills to effectively design, develop, implement and manage medium to large-scale data warehouse systems.
- Technology Literacy: Students will master technologies used to develop and deploy data warehouses and analytics systems.
- Knowledge Integration: Students will be able to analyze business requirements across multiple industries and address these requirements with appropriate data warehousing and analytics technologies.
- Written communication: Students will analyze a business and develop and write a business analytics proposal that will be implemented during the semester.
- Oral communication: Students will present their business analytics solution
- Teamwork and Leadership: Students will work in groups to analyze a business and develop and write a business analytics proposal that will be implemented during the semester.
- Ethical Awareness: Students will discuss issues of privacy, customer data collection and management, energy use by data centers, and ethical concerns when collecting, analyzing and presenting analytical data.
- Mid term Exam: 25%
- Final Exam: 35%
- Homeworks and Class participation: 20%
- Oracle or SQL Server Data Warehouse Project: 20%
- The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence. by Ralph Kimball, Margy Ross and Warren Thornthwaite. Wiley; First Edition (February 8, 2010). ISBN-10: 0470563109
- Oracle Business Intelligence 11g Developers Guide. by Mark Rittman. McGraw-Hill Osborne Media; First Edition (September 18, 2012). ISBN-10: 0071798749
- Additional course materials will be provided on the course WWW Home page.
- This course involves some programming assignments. Introductory notes on various programming languages can be found linked on the course home page.
- Computer Labs: VC 11-125 (others to be announced)
- Academic Journals (e.g., CACM, IEEE CS) and Trade Magazines (e.g., Information Week, PC Week)
- Course Introduction and Review of E-R Model
- Relational Model and SQL
- Data Warehouse Project Planning
- Data Warehouse modeling and Architecture
- Extraction, Translation and Loading (ETL) and ELT
- Oracle Warehouse Builder exercise
- OLAP Data Structures / Construction
- Business Analytics mapping
- OLAP Data Structures / Construction
- Query Processing, Optimization and Performance
- BI Reporting tools exercise
- Mid term exam
- Data Warehouse Administration and Security
- Column-store and NoSQL Databases
- Distributed Data Processing Architectures
- HADOOP Exercise
- Web Applications Integration, XML and semi-structured data analytics
- Complex Event Processing / Streaming Databases
- Open Source BI systems
- Final Exam Review
- Project Results Presentations