Data Analytics with R Certification Training

Become a skilled data analyst with specialization in R programming language

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Key Highlights

  • 30 hours of Instructor-led Live Sessions
  • Complimentary Self-paced course of ?Statistical Essentials for R?
  • 15 Weekend sessions, each of 3 hours duration
  • Case Studies on Real-life Scenarios
  • Lifetime Access to Learning Management System
  • Practice Assignments
  • 24X7 Expert Support
  • Online Forum for Discussions

Course Price

$199.00 $399.00

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Available Courses Delivery

This course is available in the following formats:

Course Overview

Data Analytics with R certification is a globally accepted certification meant for professionals looking to transform themselves into skilled data analysts. It acquaints learners with Data Analytics, R Programming, Exploratory Data Analysis, Data Visualization, Data Mining, Data Manipulation, Anova & Sentiment Analysis, and Regression. It trains the learners in the entire process of developing insights into the data set, and investigating it using numerous statistical methods.

Course Objectives

  • Train about the concepts around Business Intelligence and Business Analytics
  • Acquaint learners with Recommendation Systems with functions, such as, Association Rule Mining, Item-based collaborative filtering, User-based collaborative filtering, etc
  • Make learners adept at numerous supervised machine learning techniques
  • Teach learners the method to use algorithms-Support Vector Machines, Logistic Regression, Decision Trees, etc
  • Equip learners with the procedure of Analysis of Variance (ANOVA)

Career Benefits

  • Key decision-making power
  • Multi-industry opportunities
  • Considered as an expert of a popular analytical tool
  • High paycheck
  • Profession in great demand
  • Enhanced customer/client relationship
  • Opportunity to work with big brands


  • Basic statistical knowledge (We provide a complimentary course 'Statistical Essentials for R')

Who should take up?

  • Beginners Data Analysts
  • Business Analysts
  • Statisticians
  • Professionals working in the Analytics industry
  • Professionals with Mathematics background
  • Professionals with Economics background
  • Professionals who wish to enter in the field of Data Analysis

Course Content

  • Introduction to terms like Business Intelligence, Business Analytics, Data, Information
  • How information hierarchy can be improved/introduced
  • Understanding Business Analytics and R
  • Knowledge about the R language, its 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 a 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
  • 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 subsetting methods
  • Summarize data by using functions like: str(), class(), length(), nrow(), ncol()
  • Use of functions like head(), tail(), for inspecting data
  • Indulge in a class activity to summarize data
  • dplyr package to perform SQL join in R
  • The various steps involved in Data Cleaning
  • Functions used in Data Inspection
  • Tackling the problems faced during Data Cleaning
  • Uses of the functions like grepl(), grep(), sub(), Coerce the data
  • Uses of the apply() functions
  • Import data from spreadsheets and text files into R
  • Import data from other statistical formats like sas7bdat and spss
  • Packages installation used for database import
  • Connect to RDBMS from R using ODBC and basic SQL queries in R
  • Basics of Web Scraping
  • Understanding the Exploratory Data Analysis(EDA)
  • Implementation of EDA on various datasets, Boxplots, whiskers of Boxplots
  • Understanding the cor() in R, EDA functions like summarize(), llist()
  • Multiple packages in R for data analysis
  • The Fancy plots like the Segment plot, HC plot in R
  • Understanding on Data Visualization
  • Graphical functions present in R
  • Plot various graphs like tableplot, histogram, Boxplot
  • Customizing Graphical Parameters to improvise plots
  • Understanding GUIs like Deducer and R Commander
  • Introduction to Spatial Analysis
  • Introduction to Data Mining
  • Understanding Machine Learning, Supervised and Unsupervised Machine Learning Algorithms, K-means Clustering
  • Association Rule Mining
  • User Based Collaborative Filtering (UBCF)
  • Item-Based Collaborative Filtering (IBCF)
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • The 3 elements for classification of a Decision Tree
  • Entropy
  • Gini Index
  • Pruning and Information Gain
  • Bagging of Regression and Classification Trees
  • Concepts of Random Forest
  • Working of Random Forest
  • Features of Random Forest, among others


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