Information Technology 442

ITEC 442: Data Warehousing and Visualization

Credit Hours: (4)

Prerequisites: ITEC 340 with a grade of “C” or better and STAT 200 or STAT 301.

Advanced examination of the principles of database systems to support analytical applications, studying and practicing techniques for modeling, managing, analyzing, and visualizing large data sets. Explores traditional and cloud-based software architectures for implementing the entire business intelligence pipeline: ETL process, data quality, dimensional modeling, data visualization, and machine learning algorithms. Emphasizes communicating with data (oral and written) to empower data-driven decision making.

Detailed Description of Content of the Course

Topics include: 

1. Introduction to business intelligence 

2. Data warehousing

  • a. Dimensional modeling 
  • b. Data quality, preparation, and cleaning  
  • c. ETL process: extract, transform, and load pipeline 

3. Data Exploration, Analysis, and Visualization 

  • a. Principles of data visualization
  • b. Online Analytical Processing (OLAP) 
  • c. Defining key performance indicators and dashboard design

4. Machine Learning Algorithms

  • a. Frequent Pattern Analysis
  • b. Automatic Cluster Detection
  • c. Classification: decision trees and neural networks 

VI. Detailed Description of Conduct of Course:

Blended delivery combines video lectures, online resources, class discussion, and hands-on learning activities. Students use industry-leading tools and one or more enterprise level database management systems to explore, analyze, manage, process, and visualize data. 

Student Learning Outcomes

Students who complete the course will be able to:    

  1. Communicate effectively with data. 
  2. Explain how business intelligence enables people to make better decisions. 
  3. Design a comprehensive business intelligence architecture. 
  4. Design and develop a basic Star schema.
  5. Design and develop a basic ETL data pipeline.
  6. Describe the importance of data quality and explain the challenges of maintaining high quality data.
  7. Design and develop effective visualizations and dashboards. 
  8. Explain machine learning algorithms. 

Assessment Measures

A significant component of the assessment must measure each individual student’s mastery of the conceptual and applied knowledge and skills described in the course objectives. Evaluations may include but are not limited to assignments, projects, papers, presentations, quizzes, and examinations.  

Review and Approval 

September 25, 2001         New course proposal      John P. Helm, Chair
October 26, 2007              Update                         Art Carter, Chair
November XX, 2010          Update                         Art Carter, Chair

Revised: June 1, 2012

Revised: 2014

March 01, 2021

Revised June, 2023