## R Programming:

## –>Intro to R Programming

## Introduction to R

Business Analytics

Analytics concepts

The importance of R in analytics

R Language community and eco-system

Usage of R in industry

Installing R and other packages

Perform basic R operations using command line

Usage of IDE R Studio and various GUI

## –>R Programming Concepts

The datatypes in R and its uses

Built-in functions in R

Subsetting methods

Summarize data using functions

Use of functions like head(), tail(), for inspecting data

Use-cases for problem solving using R

## –>Data Manipulation in R

Various phases of Data Cleaning

Functions used in Inspection

Data Cleaning Techniques

Uses of functions involved

Use-cases for Data Cleaning using R

## –>Data Import Techniques in R

Import data from spreadsheets and text files into R

Importing data from statistical formats

Packages installation for database import

Connecting to RDBMS from R using ODBC and basic SQL queries in R

Web Scraping

Other concepts on Data Import Techniques

## –>Exploratory Data Analysis (EDA) using R

What is EDA?

Why do we need EDA?

Goals of EDA

Types of EDA

Implementing of EDA

Boxplots, cor() in R

EDA functions

Multiple packages in R for data analysis

Some fancy plots

Use-cases for EDA using R

## –>Data Visualization in R

Storytelling with Data

Principle tenets

Elements of Data Visualization

Infographics vs Data Visualization

Data Visualization & Graphical functions in R

Plotting Graphs

Customizing Graphical Parameters to improvise the plots

Various GUIs

Spatial Analysis

Other Visualization concepts