Course Overview
R Programming course helps you to learn how to program in R and how to use R for effective data analysis. Things like how to install and configure software which are necessary for statistical programming environment will be learnt discussing generic programming language concepts as they are implemented on a highlevel statistical language. This course completes the practical issues in statistical computing which includes programming in R Language, reading data into R, accessing R packages, writing functions in R, debugging, and organizing and commenting in R code
Requirements
 Students should have knowledge of basic statistics and know the difference between descriptive and inferential statistics.
 Extensive previous experience in a modern programming language is required.
Curriculum

Day 2
 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
 Handson 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
 Handson with Sample data

Day 1
 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
 Ndimensional 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

Day 3
 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
 Handson 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