Fundamentals of Data Mining

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Credit Hours: 3

Course Contents:

• Introduction to Data Mining
• Getting to know Data
• Preprocessing
• Data Warehousing and On-line Analytical Processing (OLAP)
• Data Cube Technology
• Mining Frequent Patterns, Associations and Correlations
• Classification
• Clustering Analysis
• Outlier Analysis
• Data Mining Trends

Recommended Book:

Data Mining: Concepts and Techniques

Grading:

• Mid Term Exam: 30 Marks
• Terminal Exam: 40 Marks
• Quizes: 15 Marks
• Assignments: 15 Marks

Related software and useful online resources:

• Download R
• Download RStudio Note: RStudio Desktop Open Source License is free.
• Quick-R by DataCamp
• OLTP and OLTP vs OLAP