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
Understanding the cor() in R
EDA functions like summarize()
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
Data Mining: Association Rule Mining And Sentiment Analysis
Understand Association Rule Mining in R and sentiment analysis.
Association Rule Mining in R
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
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
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.
- 10 Days
- 0 Units
- 0 Hrs