Swayam
Free Online Course
English
Paid Certificate Available
12 weeks long
selfpaced
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 : NILL PREREQUISITES : Basic Statistics Knowledge INDUSTRY SUPPORT : Big Data companies, Analytics & Consultancy companies, Companies with Analytics Division
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
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
Syllabus
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
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
Taught by
Gaurav Dixit
