 ## R Programing

### Module 1: Basic

• R Installation
• R Studio
• Understanding Data Structures in R – Lists
• Matrices, Vectors
• R Studio the IDE
• Basic Building Blocks in R
• Understanding Vectors in R
• Basic Operations Operators and Types
• Handling Missing Values in R
• Matrices and Data Frames in R Logical Statements in R

### Module 2: Data Visualization

• Grammar of Graphics
• Bar Charts
• Histograms
• Pie Charts
• Scatter Plots
• Line Plots and Regression
• Word Clouds
• Box Plots
• GGPLOT2

### Module 3: Statistical Learning and ANOVA

• Measures of Central Tendency in Data
• Measures of Dispersion
• Understanding Skewness in Data
• Probability Theory
• Bayes Theorem
• Probability Distributions
• Hypothesis Testing
• One-Way Analysis of Variance
• Assumption of ANOVA
• Statistics Associated with One-Way Analysis of Variance
• Interpreting the ANOVA Results
• TwoWay Analysis of Variance
• Interpreting the ANOVA Results
• Analysis of Covariance

### Module 4: Regression

• What is Regression Analysis
• Limitations of Regression
• Covariance and Correlation
• Multivariate Analysis
• Assumptions of Linearity Hypothesis Testing
• Limitations of Regression
• Implementing Simple & Multiple Linear Regression
• Making Sense of Result Parameters
• Model Validation
• Handling Other Issues/Assumptions in Linear Regression
• Handling Outliers
• Categorical Variables
• Autocorrelation
• Multicollinearity
• Heteroskedasticity Prediction and Confidence Intervals

### Module 5: Regression

• Implementing Logistic Regression
• Making Sense of Result Parameters: Wald Test
• Likelihood Ratio Test Statistic
• Chi-Square Test Goodness of Fit Measures
• Model Validation: Cross Validation
• ROC Curve
• Confusion Matrix

### Module 6: Decision Trees and Random Forest

• Introduction to Predictive Modelling with Decision Trees
• Entropy & Information Gain
• Standard Deviation Reduction (SDR)
• Overfitting Problem
• Cross Validation for Overfitting Problem
• Running as a Solution for Overfitting