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Overview

Objective of this course is to impart knowledge on use of data mining techniques for deriving business intelligence to achieve organizational goals. Use of R (statistical computingCSS - MOOCs Proposal software) to build, assess, and compare models based on real datasets and cases with an easy-to-follow learning curve.

INTENDED AUDIENCE : NILLPREREQUISITES :Basic Statistics KnowledgeINDUSTRY SUPPORT : Big Data companies, Analytics & Consultancy companies, Companies with Analytics Division

Syllabus

COURSE LAYOUT

Week1:General Overview of Data Mining and its Components Introduction and Data Mining Process Introduction to R Basic Statistical Techniques
Week2:Data Preparation and Exploration Visualization Techniques
Week3:Data Preparation and Exploration Visualization Techniques Dimension Reduction Techniques Principal Component Analysis
Week4:Performance Metrics and Assessment Performance Metrics for Prediction and Classification
Week5:Supervised Learning Methods Multiple Linear Regression
Week6:Supervised Learning Methods Multiple Linear Regression
Week7:Supervised Learning Methods Naà ̄ve Bayes
Week8:Supervised Learning Methods Classification & Regression Trees
Week9:Supervised Learning Methods Classification & Regression Trees
Week10:Supervised Learning Methods Logistic Regression
Week11:Supervised Learning Methods Logistic Regression Artificial Neural Networks
Week12:Supervised Learning Methods and Wrap Up Artificial Neural Networks Discriminant Analysis Conclusion