• Introduction to R Programming
• Why R language
• Installation of R & R Studio
• Classes & Objects
• Basic data types in R
• Vector in R 
• Matrix & Factor in R 
• N-dimensional Array in R
• Data Frames in R 
• Plotting using gggplot2 in R – Scatter plot, Box plot, Histogram, Bar chart 
• List in R
• Table function in R
• Statistics in R – Mean, Median, Mode, Range, Variance, SD, Inter Quartile 
• Get data from MySQL using R
• Get data from website using R
• Apply & Dplyr functions in R
• Labs/Hands –on

• Steps involved in solving an Analysis Use case
• Data preprocessing/preparation in R
• Missing data, Categorical data, Feature Scaling, Splitting data to test & train sets
• Regression Algorithm- Simple Linear Regression
• Understand Cost Function, weight & Bias
  • Use Case: Create a simple model using x & y
• Classification Algorithm- K Nearest Neighbor
  • Use Case: Create a Model to predict the species of flowers
• Hands-on with Sample data
• Clustering Algorithm- K means
• Elbow Method in K means to predict optimal no. of Clusters
• Clustering Algorithm- Hierarchical Clustering
• Dendograms in Hierarchical Clustering to predict optimal no. of Clusters
• Use Case: Using K means & HC to extract patterns to analyze crime in different cities

Hands-on with Sample data

• Logistics Regression
• How to create and read ROC curve
• How to check the accuracy of the Model using Confusion Matrix
• Use Case: Create a Model to predict Customer Churn
• Hands-on with Sample data
• Random Forest using Decision Trees
• Use Case: Satellite Image Classification using Random Forest. 
• Create a Model to identify/classify different types of land e.g. barren, forest, urban, river from a Satellite image
• How to check the accuracy of the Model using Confusion Matrix
• Support Vector Machine for Classification
  • Use Case:  Character Recognition using Random Forest 
• Polynomial Regression

Use Case: Create a Model to using Polynomial Regression