Section outline


  • 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