Students are provided with theoretical knowledge and practical skills in advanced topics in database systems, data marts, and data warehouses. The specific topics covered include indexing methods, query processing and optimization strategies for relational database systems, Object Relational Mapping and Object Database design, distributed database systems, data mining on large databases.

Learning Objectives and Outcomes:

By the end of this course students will be able to:

1. Examine application domains, concepts, and structures of industry-standard database management systems, including web and cloud systems.
2. Evaluate emerging technologies (such as Big Data, NoSQL, On-Line Analytical Processing (OLAP), and Data Warehouses) and their potential as business solutions.
3. Compare and contrast Data Marts and Data Warehouses, and the advantages or disadvantages of each using research-based evidence.

Course Schedule and Topics

This course will cover the following topics in eight learning sessions, with one Unit per week.

Week 1: Unit 1 – Data Warehouse Requirements

Week 2: Unit 2 – Design Requirements for Data Warehouse

Week 3: Unit 3 – Data Warehouse ETL process

Week 4: Unit 4 – Data Mining Techniques

Week 5: Unit 5 – Managing Data on the Web

Week 6: Unit 6 – Managing Data in the Cloud

Week 7: Unit 7 – Data Analysis Tools

Week 8: Unit 8 – Business Intelligence/Data Warehouses

Learning Guide

The following is an outline of how this course will be conducted, with suggested best practices for students.

Unit 1: Data Warehouse Requirements

• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 2: Design Requirements for Data Warehouse

• Peer assess Unit 1 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 3: Data Warehouse ETL process

• Peer assess Unit 2 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 4: Data Mining Techniques

• Peer assess Unit 3 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 5: Managing Data on the Web

• Peer assess Unit 4 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 6: Managing Data in the Cloud

• Peer assess Unit 5 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 7: Data Analysis Tools

• Peer assess Unit 6 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 8: Business Intelligence/Data Warehouses

• Peer assess Unit 7 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete the Reflective Portfolio Assignment
• Complete and submit the anonymous Course Evaluation