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 Learning Outcome: (at the end of the course, student will be able to do:)

• Text Book

• Reference Reading Materials

1. Introduction to Data Mining and Applications
2. Data Mining Concepts and Techniques
3. Data Mining Techniques
4. Data Mining using Weka
5. Weka Manual
6. Data Mining using Python

• Global Data Repository for Data Mining

1. WISDM
   
2. UCI ML Repository
   
3. KDD Cup
   
4. Kaggle
   
5. KDnuggets
   

• Standard Templates

1. IEEE Template
2. ACM Template