Course Rationale
An introduction to data mining; Data preparation, model building, and data mining techniques such as clustering, decisions trees and neural networks; Induction of predictive models from data: classification, regression, and probability estimation; Application case studies; Data-mining software tools review and comparison.

Course Objectives

• To appreciate the necessity of data mining in everyday life
• To apply the concept of data mining in solving problems
• To demonstrate applications of data mining using tools
• To apply knowledge of data mining in project work

Course Outcomes (CO’s)

• CO1 Able to grasp the basic Data Mining Principles
• CO2 Able to identify appropriate data mining algorithms to solve real world problems
• CO3 Able to compare and evaluate different data mining techniques like classification, prediction, clustering and association rule mining
• CO4 Able to apply data mining knowledge in problem solving

• Text Book

• Reference Reading Materials

1. Introduction to Data Mining and Applications
2. Data Mining Concepts and Techniques
3. Data Mining Techniques

• Standard Templates

1. IEEE Template
2. ACM Template