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 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 possess the basic knowledge of Weka and Python concerning data mining and machine learning
• CO2 Able to implement different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining to solve real-world problems using Weka and Python
• CO3 Able to compare and evaluate different data mining and machine learning algorithms like classification, prediction, clustering and association rule mining using Weka and/or Python
• CO4 Able to apply implementation knowledge of data mining and machine learning in developing research ideas

• Recommended Books

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 and/or Machine Learning

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

• Standard Templates

1. IEEE Template
2. ACM Template