BUSINESS ANALYTICS WITH R Training

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BUSINESS ANALYTICS WITH R Training


 (4.9) | 750 Ratings


Introduction


BUSINESS ANALYTICS WITH R Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 30 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course 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.

Business Analytics with R project

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Course 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.

Business Analytics with R project

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