Data Science with R

ONE – Introduction to Business Analytics

  • Overview
  • Business Decisions and Analytics
  • Types of Business Analytics
  • Applications of Business Analytics
  • Data Science Overview
  • Conclusion
  • Knowledge Check

TWO – Introduction to R Programming

  • Overview
  • Importance of R
  • Data Types and Variables in R
  • Operators in R
  • Conditional Statements in R
  • Loops in R
  • R Script
  • Functions in R
  • Conclusion
  • Knowledge Check

THREE – Data Structures

  • Overview
  • Identifying Data Structures
  • Demo: Identifying Data Structures
  • Assigning Values to Data Structures
  • Data Manipulation
  • Assigning values and applying functions

FOUR – Data Visualization

  • Overview
  • Introduction to Data Visualization
  • Data Visualization using Graphics in R
  • ggplot2
  • File Formats of Graphic Outputs

FIVE – Statistics for Data Science – I

  • Overview
  • Introduction to Hypothesis
  • Types of Hypothesis
  • Data Sampling
  • Confidence and Significance Levels

 

SIX – Statistics for Data Science – II

  • Overview
  • Hypothesis Test
  • Parametric Test
  • Non-Parametric Test
  • Hypothesis Tests about Population Means
  • Hypothesis Tests about Population Variance
    • Hypothesis Tests about Population Proportions
    • Conclusion
    • Knowledge Check

      SEVEN – Regression Analysis

      • Types of Regression Analysis Models
      • Linear Regression
      • Simple Linear Regression
      • Non-Linear Regression
      • Regression Analysis with Multiple Variables
      • Cross Validation
      • Non-Linear to Linear Models
      • Principal Component Analysis
      • Factor Analysis

        EIGHT – Classification

        • Overview
        • Classification and its Types
        • Logistic Regression
        • Support Vector Machines
        • Demo: Support Vector Machines
        • K-Nearest Neighbors
        • Naive Bayes Classifier
        • Demo: Naive Bayes Classifier
        • Decision Tree Classification
        • Demo: Decision Tree Classification
        • Random Forest Classification
        • Evaluating Classifier Models
        • K-Fold Cross Validation

         

        LESSON NINE – Clustering

      • Overview
        Introduction to Clustering
        Clustering Example
        Clustering Methods: Prototype Based Clustering
        Demo: K-means Clustering
        Clustering Methods: Hierarchical Clustering
        Hypothesis Tests about Population Proportions
        Conclusion
        Knowledge Check

        LESSON SEVEN – Regression Analysis

        Overview
        Introduction to Regression Analysis
        Types of Regression Analysis Models
        Linear Regression
        Simple Linear Regression
        Non-Linear Regression
        Regression Analysis with Multiple Variables
        Cross Validation
        Non-Linear to Linear Models
        Principal Component Analysis
        Factor Analysis

        LESSON EIGHT – Classification

        Overview
        Classification and its Types
        Logistic Regression
        Support Vector Machines
        Demo: Support Vector Machines
        K-Nearest Neighbours
        Naive Bayes Classifier
        Demo: Naive Bayes Classifier
        Decision Tree Classification
        Demo: Decision Tree Classification
        Random Forest Classification
        Evaluating Classifier Models
        K-Fold Cross Validation

        LESSON NINE – Clustering

        Overview
        Introduction to Clustering
        Clustering Example
        Clustering Methods: Prototype Based Clustering
        Demo: K-means Clustering
        Clustering Methods: Hierarchical Clustering

        Total Hours: 60

      • The same Program can be done with simulated projects for 1 month or more.

       

Categories: ,