## R Programming Training

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 high-level 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

3 Days

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.

#### 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
• 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

#### 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
• 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

#### 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
• 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