Information Technology 542

ITEC 542: Data Warehousing, Mining, and Reporting

Prerequisite: Admission into the Data and Information Management program, or permission of instructor

Credit hours (3)

Advanced examination of the principles of database systems covering techniques for modeling, managing, and analyzing large data sets. The course covers the architectural components to support enterprise level business intelligence with in-depth coverage of the dimensional model, data integration, reporting, data visualization, performance dashboards, machine learning algorithms, and application of common data mining techniques. Students will design and develop an end-to-end business intelligence application including a data warehouse, an extract, transform, and load (ETL) process, a dashboard, reports, and a data mining application. Students must have completed a databse course including hands-on experience with the relational model, SQL, security, databse design, and stored procedures.

Detailed Description of Course

• Introduction to business intelligence
• Data Warehousing
     a. Dimensional modeling
    b. Warehouse aggregates
    c. Data quality
    d. Extract, transform, and load (ETL) process
    e. Physical design
     f. Data warehousing lifecycle
• Reporting and data analysis
    a. Online analytical processing (OLAP)
    b. Commercial query and reporting tools
• Data mining
    a. Data mining methodology
    b. Statistical methods
    c. Decision trees
    d. Association rules
    e. Clustering
    f. Neural networks
    g. Data preparation

Detailed Description of Conduct of Course

The course will be delivered in a lecture and discussion format with demonstration and application of concepts using one or more enterprise level database management systems.

Goals and Objectives of the Course

Students who complete this course will be able to:
• Design and develop a Star schema and describe best practices for dimensional modeling.
• Design and develop a basic ETL process and explain the challenges of the ETL process.
• Identify and develop valuable aggregates for a given problem.
• Design and develop different types of reports and reporting requirements.
• Describe the limitations of SQL with respect to analytical reports.
• Describe common data mining tasks.
• Describe data mining techniques and implement at least one technique.
• Explain the value of transactional data with respect to business intelligence.
• Explain the importance of data quality and the challenges of producing high quality data.

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, presentations, quizzes, and examinations.

Other Course Information


Review and Approval

May 1, 2018
April 23, 2014