Business Analytics with R Training

Business Analytics with R Training course is developed to help learners grasp the real-time knowledge and become an expert in Business Analytics. Training teaches all the steps required to import various formats of Data with advanced R.

 

Business Analytics with R Training Curriculum

Introduction To Business Analytics

In this module, you will understand what is R language, business analytics with R, Installing R and much more…

Understand Business Analytics and R
Knowledge on the R language
Community and ecosystem
Understand the use of ‘R’ in the industry
Compare R with other software in analytics
Install R and the packages useful for the course
Perform basic operations in R using command line
Learn the use of IDE R Studio and Various GUI
Use the ‘R help’ feature in R
Knowledge about the worldwide R community collaboration.
Introduction To R Programming

R language is widely used among statisticians and data miners for developing statistical software and data analysis.

The various kinds of data types in R and its appropriate uses
The built-in functions in R like: seq(), cbind (), rbind(), merge()
Knowledge on the various Sub-setting methods in R
Summarize data by using functions like: str(), class(), length(), nrow(), ncol() in R
Use of functions like head(), tail() for inspecting data
Indulge in a class activity to summarize data in R
Data Manipulation In R

One of the most important aspects of computing with data is the ability to manipulate it, to enable subsequent analysis and visualization. R offers a wide range of tools for this purpose.

The various steps involved in R for Data Cleaning
Functions used in R for Data Inspection
Tackling the problems faced during Data Cleaning
Uses of the functions like grep(), sub()
Coerce the data in R
Uses of the apply() functions.
Data Import Techniques In R

This module describes how to enter or import data into R, and how to prepare it for use in statistical analyses.

Import data from spreadsheets and text files into R
Import data from other statistical formats like sas7bdat and spss into R
Package installation used for database import
Connect to RDBMS from R using ODBC and basic SQL queries in R
Basics of Web Scraping in R
R Exploratory Data Analysis

Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set.

Understanding the Exploratory Data Analysis (EDA)
Implementation of EDA on various datasets in R
Box plots
Understanding the cor() in R
EDA functions like summarize()
llist()
Multiple packages in R for data analysis
The Fancy plots like Segment plot and HC plot in R.
Data Visualization In R

One of the most appealing things about R is its ability to create data visualizations with just a couple of lines of code.

Understanding on Data Visualization
Graphical functions present in R
Plot various graphs in R like table plot, histogram, box plot
Customizing Graphical Parameters to improvise the plots
Understanding GUIs like Deducer and R Commander
Introduction to Spatial Analysis in R
Data Mining: Clustering Techniques

This module will concentrate on k means clustering techniques.

Introduction to Data Mining in R
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms in R
K-means Clustering.
Data Mining: Association Rule Mining And Sentiment Analysis

Understand Association Rule Mining in R and sentiment analysis.

Association Rule Mining in R
Sentiment Analysis.
Linear And Logistic Regression In R

We’ll demonstrate linear regression and logistic regression in this module

Linear Regression in R
Logistic Regression in R
Anova And Predictive Analysis In R

In this module, learn about Anova and predictive analysis techniques in R

Anova
Predictive Analysis.
R Data Mining: Decision Trees And Random Forest

This module will enlighten you over the decision trees in R, classification Rules, concepts of random forest and much more..

Decision Trees in R
Algorithm for creating Decision Trees
Greedy Approach: Entropy and Information Gain
Creating a Perfect Decision Tree in R
R Classification Rules for Decision Trees
Concepts of Random Forest in R
Working of Random Forest in R
Features of Random Forest in R
Project

This module discusses the concepts taught throughout the course and their implementation in a Project..

Analyse Census Data
To predict insights on the income of the people based on the factors like : Age, education, work-class, occupation, etc.

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