CIS 9440 – Sample Course Syllabus

Title Data Warehousing and Analytics
Description 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 Extract-Transform-Load (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.
Prerequisites CIS 9340
Learning Goals

The main objective of this course is to provide students with an in-depth understanding of the design and implementation of database warehousing and analytics database systems.

Topics Include:

  • Brief Review of Relational Design and SQL
  • Data Warehouse modeling and Architecture
  • Extraction, Translation and Loading
  • Query Processing and Optimization
  • Data Warehouse Administration and Security
  • Column-store and NoSQL Databases
  • Distributed Data Processing: Hadoop
  • Streaming Databases / Complex Event Processing
  • Open Source BI Tools

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.
Grades
  • Mid term Exam                                                   25%
  • Final Exam                                                          30%
  • Homeworks and Class participation                    20%
  • Oracle or SQL Server Data Warehouse Project     25%
Textbooks and other material
  • Business Intelligence Guidebook: From Data Integration to Analytics by Rick Sherman. Elsevier Science & Technology (Nov 07, 2014). ISBN: 978-0-12-411461-6
  • (Recommended) 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
  • (Recommended) The Data Warehouse Toolkit (3rd. Edition). by Ralph Kimball and Margy Ross. Publisher: Wiley Edition: 3, Year Published: 2013, ISBN: 978-1-118-53080-1
  • This course involves some programming assignments. Introductory notes on various programming languages can be found linked on the course home page.
  • Oracle Database tools used this semester include Oracle 11g R2 Enterprise Edition (not Express Edition) that includes Oracle Warehouse Builder and Oracle Business Intelligence.
  • Academic Journals (e.g., CACM, IEEE CS) and Trade Magazines (e.g., Information Week, PC Week)
Topics
  • Course Introduction and Review of E-R Model
  • Relational Model and SQL
  • Data Warehouse Project Planning
  • Data Warehouse Architecture
  • Dimensional Modeling
  • Dimensional Modeling (Continued)
  • Extraction, Translation and Loading (ETL) and ELT
  • Extraction, Translation and Loading (ETL) and ELT (Cont.)
  • Query Processing, Optimization and Performance
  • Review for Mid Term Exam
  • 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