R Analytics Online Training | R Certification Course | Edureka

Data Analytics with R Certification Training

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Data Analytics with R training will help you gain expertise in R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio for real life case studies on Retail, Social Media.
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Why should you take Data Analytics with R course ?

R is the most popular data analytics tool owing to it being open-source, its flexibility, packages and community

"R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.

The average salary for a Senior Data Scientist skilled in R is $123k (Payscale salary data)

Instructor-led Data Analytics with R live online classes

08 th  Feb
Fri & Sat(5 Weeks) Weekend Batch Timings : 09:30 PM - 12:30 AM (EST)
Sold Out
16 th  Mar
Sat & Sun (5 Weeks) Weekend Batch Timings : 10:00 AM - 01:00 PM (EST)
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Course Price

350 389
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EMI starts at 4498 / month.

Learning Objectives - This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems. This module tells you what is R and how it is used by the giants like Google, Facebook, Bank of America, etc. Also, you will learn use of 'R' in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

Topics - 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 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.

Learning Objectives - This module starts from the basics of R programming like datatypes and functions. In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario. You will also learn how to apply the 'join' function in SQL.

Topics - 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

Learning Objectives - In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis. Thus using and exploring the popular functions required to clean data in R.

Topics - 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.

Learning Objectives - This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website. This module teaches you various data importing techniques in R.

Topics - 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.

Learning Objectives - In this module, you will learn that exploratory data analysis is an important step in the analysis. EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

Topics - 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.

Learning Objectives - In this module, you will learn that visualization is the USP of R. You will learn the concepts of creating simple as well as complex visualizations in R.

Topics - 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.

Learning Objectives - This module lets you know about the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types. We will also discuss the process involved in 'K-means Clustering', the various statistical measures you need to know to implement it in this module.

Topics - Introduction to Data Mining, Understanding Machine Learning, Supervised and Unsupervised Machine Learning Algorithms, K-means Clustering.

Learning Objectives - In this module, you will learn how to find the associations between many variables using the popular data mining technique called the "Association Rule Mining", and implement it to predict buyers' next purchase. You will also learn a new technique that can be used for recommendation purpose called "Collaborative Filtering". Various real-time based scenarios are shown using these techniques in this module.

Topics - Association Rule Mining, User Based Collaborative Filtering (UBCF), Item Based Collaborative Filtering (IBCF)

Learning Objectives - This module touches the base of 'Regression Techniques’. Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.

Topics - Linear Regression, Logistic Regression.

Learning Objectives - This module tells you about the Analysis of Variance (Anova) Technique. The algorithm and various aspects of Anova have been discussed in this module. Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and mine live data from Twitter to find out the sentiment of the tweets.

Topics - Anova, Sentiment Analysis.

Learning Objectives - This module covers the concepts of Decision Trees and Random Forest. The algorithm for creation of trees and classification of decision trees and the various aspects like the Impurity function Gini Index, Pruning, Entropy etc are extensively taught in this module. The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples. At the end of the class, these concepts are implemented on a real-life data set.

Topics - 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.

Learning Objectives - This module discusses various concepts taught throughout the course and their implementation in a project.

Topics - Analyze census data to predict insights on the income of the people, based on the factors like: age, education, work-class, occupation using Decision Trees, Logistic Regression and Random Forest. Analyze the Sentiment of Twitter data, where the data to be analyzed is streamed live from twitter and sentiment analysis is performed on the same.

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edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc.

After the completion of the Edureka Data Analytics with R course, you should be able to:

1. Understand concepts around Business Intelligence and Business Analytics

2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others

3. Apply various supervised machine learning techniques

4. Perform Analysis of Variance (ANOVA)

5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc

6. Use various packages in R to create fancy plots

7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights

The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.

Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career

Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course

This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics.

The pre-requisites for learning 'Data Analytics with R' includes basic statistics knowledge. We provide a complimentary course "Statistics Essentials for R" to all the participants who enroll for the Data Analytics with R Training. This course helps you brush up your statistics skills.

Edureka’s Data Analyst With Specialization In R Certificate Holders work at 1000s of companies like

5000 Total number of reviews
4.57 Aggregate review score
80% Course completion rate
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Durvesh Javle link Assistant Manager - Tolling and Projects at BASF India Ltd, Thane, Maharashtra, India

Good overview and insight on the real world  application and use cases of R. An interactive training method and interesting content lay path to venture and explore more on R programming.

Sandeep Shenoy link Project Lead at Mphasis an HP Company, Bengaluru, Karnataka, India

I am a beneficiary of the Business Model of edureka and i look forward to more learning on this wonderful platform. I have completed one course (Big Data and Hadoop) and on the verge of starting another (Business Analytics with R). With Edureka you can put to rest, the concerns about the quality and experience one may have.

Shaunak Ponkshe link Business Analyst Team Lead

Hi. This Is really the best Institute where I learn the business analyst R skills and got trained by the instructor to reach right path.

Rahul RK link Senior Consultant with Bristlecone, New Delhi Area, India

Detailed discussion on the topic in the virtual classes, which helped in developing the basics correctly. All this with awsome 24*7 support. Great work edureka!

Mohit Sharma

I have been subscribing to Edureka's courses for almost a year now, primarily related to Big Data and Data analytics. These courses have helped me to gain that competitive edge which is required at the job. Also, their courses cover a breadth of topics and range from computer programming languages like Java to Data Visualisation. There is also constant updation done on these courses, and you can talk to their support staff at any time for any assistance. I found the faculties very knowledgeable, and all the courses that I enrolled in were delivered in a very detailed and professional manner. For any person looking for online training, I can recommend Edureka without hesitation.

Ranjith Kumar

Instructor very kindly replied to our questions with real time scenarios. This actually helped us to understand the concepts quickly.

Ravi Chintamani Program Manager, SAP Supply Chain, Functional Expert at Tech Mahindra

Very good and well presented R course. Trainer has a personable style reassuring online participants to learn well which is unique. This helps to learn quickly. Keep it up. Support staff, thanks for your unstinting 24*7 support. Overall very well executed classes

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Your system should have minimum 4GB RAM and i3 processor or above. 
For doing your practicals, you would need to install R set-up or R-Studio on your system. The step-wise installation guides for setting up the environment on various operating systems are present in the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.
Towards the end of the Course, you will be working on a live project. You can choose any of the following as your Project work:

Project #1: Sentiment Analysis of Twitter Data
Industry : Social Media
Description : A sports gear company is planning to brand themselves by putting their company logo on the jersey of an IPL team. We assume that any team which is more popular on twitter will give a good ROI. So, we evaluate two different teams of IPL based on their social media popularity and the team which is more popular on twitter will be chosen for brand endorsement. The data to be analyzed is streamed live from twitter and sentiment analysis is performed on the same. The final output involves a comparable visualization plot of both the teams, so that the clear winner can be seen. 

The following insights need to be calculated :
1) Setup connection with twitter using twitter package. And perform authentication using handshake function.
2) Import tweets from the official twitter handle of the two teams using SearchTwitter function.
3) Prepare a sentiment function in R, which will take the arguments and find its negative or positive score.
4) Score against each tweet should be calculated.
5) Compare the scores of both the teams and visualize it.

Project #2: Census Data Analysis
Industry : Government Dataset
Description : Analyze the census data and predict whether the income exceeds $50K per year. Follow end to end modelling process involving:
1) Perform Exploratory Data Analysis and establish hypothesis of the data.
2) Test for Multi col-linearity, handle outliers and treat missing data.
3) Create training and validation data sets using Stratified Random Sampling (SRS) of data.
4) Fit Classification model on training set (Logistic Regression/Decision Tree) 
5) Perform validation of the models (ROC curve, Confusion Matrix) 
6) Evaluate and freeze the final model.

Additional Resources:
Here is the list of few additional case studies that you will get at edureka for deeper understanding of R applications.

Study#1: Market Basket Analysis
Industry: Retail - CPG
Description: Market Basket Analysis is done to see if there are combinations of products that frequently co-occur in transactions. The analysis gives clues as to what a customer might have bought if the idea had occurred to them. This is done using the “Association Rules” on real-time data. In this case study, you shall understand various methods for finding useful associations in large data sets using statistical performance measures. You will also learn how to manage the peculiarities of working with transaction data.

Data-set: The data set used here is from a grocery super store with 9835 rows of free flowing data without any labels.

Study#2: Strategic Customer Segmentation for Retail Business
Industry: E-Commerce, Retail
Description: In this case study, we will consider the dataset from a UK-based online retail business for the last two years. The objective of this case study is to do customer segmentation in this data set. 
For this exercise, we are going to use customer’s recency, frequency and monetary (RFM) values. From these three derived values, we will segment entire customer base and will generate insights on the data set provided to do customer segmentation using RFM Model based Clustering Analysis.

Data-set: comprises 0.5 million records and 8 variables. Each record is for one online order placed by the customer.

Study#3: Pricing Analytics and Price Elasticity
Industry: Retail
Description: A retailer is planning to sell a new type of cheese in some of its stores. This is a pilot project for the retailer & based on the data collected during this pilot phase, retailer wants to understand a few things.
To promote sales of cheese, the retailer is planning for two different types of in-store advertisement: 
1) Cheese as a natural product 
2) Cheese as a family caring product

Now the retailer wants to know:
1) Which in-store advertisement theme is better and giving better sales of cheese in the store?
2) How the sales of cheese is reacting to its price change i.e. price elasticity?
3) What is the impact of the price changes of other products in the same store (e.g. Ice-cream & Milk) on the sales of cheese i.e. cross-price elasticity.
4) What should be the best price of cheese to maximize the sales and then do sales forecast.

Data-set: The data set used in this case study will have the following columns - 
1) Price of Cheese 
2) Sales of Cheese 
3) Advertising method for cheese (either as a natural product or as a family product) 
4) Price of Ice cream 
5) Price of Milk

Study#4: Clustering Application using Shiny
Industry: Consumer Packaged Goods
Description: Shiny turn your analyses into interactive web applications, it is a web application framework for R. The data set that we are using in this case study relates to the clients of a wholesale distributor. It comprises, the annual spending in monetary units (m.u.) on diverse product categories. With this data we want to create a web based shiny application which can segment customers of wholesale distributor based upon the parameter passed thru ui.r

Data-set: The data set used in this case study has 440 rows of data and has the following attributes in columns - 
1) Channel
2) Region
3) Fresh
4) Milk
5) Grocery
6) Frozen
7) Detergents_Paper
8) Delicatessen

Instructor-led Sessions

30 Hours of Online Live Instructor-led Classes. Weekend class : 10 sessions of 3 hours each and Weekday class : 15 sessions of 2 hours 

Real-life Case 

Live project based on any of the selected use cases, involving implementation of R tool.


Each class has practical assignments which shall be finished before the next class and helps you to apply the concepts taught during the class.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.


edureka certifies you as a 'Data Analyst' based on your project performance, reviewed by our expert panel.


We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
"You will never lose any lecture. You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch."
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.
All our instructors are working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains. They are subject matter experts and are trained by Edureka for providing online training so that participants get a great learning experience.
You can Call us at +91 98702 76457 /1844 230 6362 ( US Tollfree ) OR Email us at sales@edureka.co . We shall be glad to assist you. 

For payments done using credit cards, to make this a No Cost EMI offer, the interest amount will be discounted from the price of the course. Your card will be charged for the course price minus the discounted interest. The total amount you will pay to the bank (excluding GST) will be equal to the price of the course. The bank will charge GST on the interest amount. For payments done using Razor Pay EMI, the price will not be discounted upfront. Instead, you will not be charged any interest for a No Cost EMI offer.
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