 # Data Science with R

## Course Overview:

The Data Science Certification with R programming training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

### Course Content:

Introduction

♦ What is R?

♦ Why R?

♦ Installing R

♦ R environment

♦ How to get help in R

♦ R Studio Overview

Understanding R data structure

♦ Variables in R

♦ Scalars

♦ Vectors

♦ Matrices

♦ List

♦ Data frames

♦ Cbind,Rbind, attach and detach functions in R

♦ Factors

♦ Getting a subset of Data

♦ Missing values

♦ Converting between vector types

Importing data

♦ Importing data from excel

♦ Accessing database

♦ Saving in R data

♦ Writing data to file

♦ Writing text and output from analyses to file

Manipulating Data

♦ Selecting rows/observations

♦ Rounding Number

♦ Creating string from variable

♦ Search and Replace a string or Number

♦ Selecting columns/fields

♦ Merging data

♦ Relabeling the column names

♦ Data sorting

♦ Data aggregation

♦ Finding and removing duplicate records

Using functions in R

♦ Apply Function Family

♦ Commonly used Mathematical Functions

♦ Commonly used Summary Functions

♦ Commonly used String Functions

♦ User defined functions

♦ local and global variable

♦ Working with dates

R Programming

♦ While loop

♦ If loop

♦ For loop

♦ Arithmetic operations

Charts and Plots

♦ Box plot

♦ Histogram

♦ Pie graph

♦ Line chart

♦ Scatterplot

♦ Developing graphs

♦ Cover all the current trending packages for Graphs

Machine Learning Algorithm

♦ Sentiment analysis with Machine learning

♦ C 5.0

♦ Support vector Machines

♦ K Means

♦ Random Forest

♦ Naïve Bayes algorithm

Statistics

♦ Correlation

♦ Linear Regression

♦ Non Linear Regression

♦ Predictive time series forecasting

♦ K means clustering

♦ P value

♦ Find outlier

♦ Neural Network

♦ Error Measure

♦ Overture of R Shiny

♦ Integration of Hadoop in R

♦ Data Mining using R

♦ Clinical research preface in R