Course description

  • Why Learn Data Science Certification?

    • The Data Science with R training in Mumbai lets you enter the world of business analytics and R by divining deep into concepts such as predictive analysis, data visualization, data exploration, etc. the course is perfect for data analysts willing to have a fast growing career in data science/analytics domain.
    • As per the reports of Randstad, the salary hikes in the field of analytics are 50% more than the IT industry.
    • researched through the report of Glassdoor that a data scientist earns a median salary of $118,709 per year.
    • The worth of the advanced analytics market in 2019 would be $29.53 Billion, as cited by

  • What are the course objectives?

    Learn how to use R language to perform different analytics techniques with our Data Science Certification with R course in Mumbai. Theoretical knowledge is incomplete without hands-on practice, and so the course includes case studies and industry-oriented projects that can be executed in CloudLab.

    Becoming proficient in R language: You can get a complete overview of the R programming language, R packages, and R-studio. You can learn how to perform data visualizations using graphics of R, acquire a sound knowledge of R data structures, and different types of apply functions along with DPYR.

    Learning advanced statistical concepts: You can get an in-depth understanding of several statistical concepts like forecasting, cluster analysis, and linear and logistic regression with our Data science training course. Hypothesis testing is also covered.

    You also get to work on hands-on projects using CloudLab as a part of the data science course. The mandatory projects include four case studies in the areas of the Internet, retail, and healthcare. R CloudLab allows hassle-free execution of the projects and provides practical experience. For better practice, four additional projects are also provided.

  • What skills you learn in Data Scientist Certification Training?

    The Data Science with R programming course in Mumbai will help the candidates to:

    • Master R programming and understand how various statements are executed in R
    • Install R, R-studio, and workspace setup, and learn about the various R packages
    • Gain a foundational understanding of business analytics
    • Understand and use the different graphics in R for data visualization
    • Define, understand and use the various apply functions and DPLYP functions
    • Gain an in-depth understanding of data structure used in R and learn to import/export data in R
    • Understand and use linear, non-linear regression models, and classification techniques for data analysis
    • Understand and use hypothesis testing method to drive business decisions
    • Gain a basic understanding of various statistical concepts
    • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
    • Learn and use the various association rules and Apriori algorithm

  • Who should take this Data Science Certification Training in Mumbai?


    Data Science is increasingly becoming popular, and there are a lot of job opportunities across various industries for professionals willing to become successful data scientists. This Data Science course provides the necessary training for professionals like:

    • Anyone with a genuine interest in the data science field
    • Graduates looking to build a career in analytics and data science
    • Experienced professionals who would like to harness data science in their fields
    • Software developers looking for a career switch into data science and analytics
    • IT professionals looking for a career switch into data science and analytics
    • Professionals working in data and business analytics

    Prerequisites: Candidates can enter the field of Data Science without any prior conditions. This Data Science course helps the candidates to understand the specifics of Data Science from the beginning.

  • What data science projects you will work on during this course?


    To provide hands-on training to the candidates, Simplilearn provides eight industry-oriented projects on R that are to be completed using CloudLab. To be eligible for the certificate, candidates must complete one of the four given projects and get it assessed.

    Project 1:

    Healthcare: Predictive analysis finds its application to mediate hospital readmissions in healthcare. Predictors, when transferred into action, prove to be utilized the most in healthcare and other areas. But historical and real-time data alone are worthless without intervention. More importantly, to judge the efficacy and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred.

    Project 2:

    Insurance: As per the survey of 2013 Insurance Predictive Modeling, predictive analytics is observed to be in rising use in insurance businesses specifically for the biggest companies. Though an increase in the use of predictive modeling was observed throughout the industry in the survey, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling, compared to 69% of companies with less than that amount of premium.

    Project 3:

    Retail: Analytics is used in optimizing product placements on shelves or optimization of inventory to be kept in the warehouses using industry examples. Through this project, participants learn the daily cycle of product optimization from the shelves to the warehouse. This gives them insights into regular occurrences in the retail sector.

    Project 4:

    Internet: Internet analytics is the collection, modeling, and analysis of user data in large-scale online services such as social networking, e-commerce, search, and advertisement. In this class, we explore a number of key functions of such online services that have become ubiquitous over the last couple of years. Specifically, we look at social and information networks, recommender systems, clustering and community detection, dimensionality reduction, stream computing, and online ad auctions.

    Four additional projects have been provided to help learners master the R language.

    Project 5:

    Music Industry: Details of listener preferences are recorded online. This data is not only used for recommending music that the listener is likely to enjoy but also to drive a focused marketing strategy that sends out advertisements for music that a listener may wish to buy. Using the demographic data, predict the music preferences of the user for targeted advertising.

    Project 6:

    Finance: You’ll predict whether someone will default or not default on a loan based on user demographic data. You’ll perform logistic regression by considering the loan’s features and the characteristics of the borrower as explanatory variables.

    Project 7:

    Unemployment: Analyze the monthly, seasonally-adjusted unemployment rates for U.S. employment data of all 50 states, covering the period from January 1976 through August 2010. The requirement is to cluster the states into groups that are alike using a feature vector.

    Project 8:

    Airline: Flight delays are frequently experienced when flying from the Washington DC area to the New York City area. By using logistic regression, you’ll identify flights that are likely to be delayed. The provided dataset helps with many variables including airports and flight times.

Course preview

    • Lesson 00 - Course Introduction

      • Course Introduction
    • Lesson 01 - Introduction to Business Analytics

      • 1.001 Overview
      • 1.002 Business Decisions and Analytics
      • 1.003 Types of Business Analytics
      • 1.004 Applications of Business Analytics
      • 1.005 Data Science Overview
      • 1.006 Conclusion
      • Knowledge Check
    • Lesson 02 - Introduction to R Programming

      • 2.001 Overview
      • 2.002 Importance of R
      • 2.003 Data Types and Variables in R
      • 2.004 Operators in R
      • 2.005 Conditional Statements in R
      • 2.006 Loops in R
      • 2.007 R script
      • 2.008 Functions in R
      • 2.009 Conclusion
      • Knowledge Check
    • Lesson 03 - Data Structures

      • 3.001 Overview
      • 3.002 Identifying Data Structures
      • 3.003 Demo Identifying Data Structures
      • 3.004 Assigning Values to Data Structures
      • 3.005 Data Manipulation
      • 3.006 Demo Assigning values and applying functions
      • 3.007 Conclusion
      • Knowledge Check
    • Lesson 04 - Data Visualization

      • 4.001 Overview
      • 4.002 Introduction to Data Visualization
      • 4.003 Data Visualization using Graphics in R
      • 4.004 ggplot2
      • 4.005 File Formats of Graphic Outputs
      • 4.006 Conclusion
      • Knowledge Check
    • Lesson 05 - Statistics for Data Science-I

      • 5.001 Overview
      • 5.002 Introduction to Hypothesis
      • 5.003 Types of Hypothesis
      • 5.004 Data Sampling
      • 5.005 Confidence and Significance Levels
      • 5.006 Conclusion
      • Knowledge Check
    • Lesson 06 - Statistics for Data Science-II

      • 6.001 Overview
      • 6.002 Hypothesis Test
      • 6.003 Parametric Test
      • 6.004 Non-Parametric Test
      • 6.005 Hypothesis Tests about Population Means
      • 6.006 Hypothesis Tests about Population Variance
      • 6.007 Hypothesis Tests about Population Proportions
      • 6.008 Conclusion
      • Knowledge Check
    • Lesson 07 - Regression Analysis

      • 7.001 Overview
      • 7.002 Introduction to Regression Analysis
      • 7.003 Types of Regression Analysis Models
      • 7.004 Linear Regression
      • 7.005 Demo Simple Linear Regression
      • 7.006 Non-Linear Regression
      • 7.007 Demo Regression Analysis with Multiple Variables
      • 7.008 Cross Validation
      • 7.009 Non-Linear to Linear Models
      • 7.010 Principal Component Analysis
      • 7.011 Factor Analysis
      • 7.012 Conclusion
      • Knowledge Check
    • Lesson 08 - Classification

      • 8.001 Overview
      • 8.002 Classification and Its Types
      • 8.003 Logistic Regression
      • 8.004 Support Vector Machines
      • 8.005 Demo Support Vector Machines
      • 8.006 K-Nearest Neighbours
      • 8.007 Naive Bayes Classifier
      • 8.008 Demo Naive Bayes Classifier
      • 8.009 Decision Tree Classification
      • 8.010 Demo Decision Tree Classification
      • 8.011 Random Forest Classification
      • 8.012 Evaluating Classifier Models
      • 8.013 Demo K-Fold Cross Validation
      • 8.014 Conclusion
      • Knowledge Check
    • Lesson 09 - Clustering

      • 9.001 Overview
      • 9.002 Introduction to Clustering
      • 9.003 Clustering Methods
      • 9.004 Demo K-means Clustering
      • 9.005 Demo Hierarchical Clustering
      • 9.006 Conclusion
      • Knowledge Check
    • Lesson 10 - Association

      • 10.001 Overview
      • 10.002 Association Rule
      • 10.003 Apriori Algorithm
      • 10.004 Demo Apriori Algorithm
      • 10.005 Conclusion
      • Knowledge Check
    • Math Refresher

      • Math Refresher
    • Lesson 1 - Course Objective

      • Learning Objectives
    • Lesson 2 - Defining Data Science

      • Learning Objectives
      • 1.1 What is data science
      • 1.2 There are many paths to data science
      • 1.3 Any advice for new data scientist
      • 1.4 What is the cloud
    • Lesson 3 - What do Data Science People do

      • Learning Objectives
      • 2.1 A day in the life of a data science person
      • 2.2 R versus Python
      • 2.3 Data science tools and technology
    • Lesson 4 - Data Science in Business

      • Learning Objectives
      • 3.1 How should companies get started in data science
      • 3.2 Recruiting for data science
    • Lesson 5 - Use Cases for Data Science

      • Learning Objectives
      • 4.1 Applications of data science
    • Lesson 6 - Data Science People

      • Learning Objectives
      • 5.1 Things data science people say
    • Lesson 1 - Welcome

      • 1.1 Welcome
      • 1.2 Learning Objectives
    • Lesson 2 - R Basics

      • 2.1 Learning Objectives
      • 2.2 Math Variables and Strings
      • 2.3 Writing Your First R Code
      • 2.4 Vectors and Factors
      • 2.5 Vector Operations
      • 2.6 Vectors and Factors
    • Lesson 3 - Data Structures in R

      • 3.1 Learning Objectives
      • 3.2 Arrays and Matrices
      • 3.3 Arrays and Matrices
      • 3.4 Lists
      • 3.5 Data Frames
      • 3.6 Lists and Dataframes
    • Lesson 4 - R Programming Fundamentals

      • 4.1 Learning Objectives
      • 4.2 Conditions and Loops
      • 4.3 Conditions and Loops
      • 4.4 Functions in R
      • 4.5 Functions in R
      • 4.6 Objects and Classes
      • 4.7 Objects and Classes
      • 4.8 Debugging
      • 4.9 Debugging
    • Lesson 5 - Working with Data in R

      • 5.1 Learning Objectives
      • 5.2 Reading CSV, Excel, and Built-in Datasets
      • 5.3 Reading Text (.txt) files in R
      • 5.4 Writing and Saving to files in R
      • 5.5 Importing Data in R
    • Lesson 6 - Strings and Dates in R

      • 6.1 Learning Objectives
      • 6.2 String Operations in R
      • 6.3 String Operations
      • 6.4 The Data Format in R
      • 6.5 Regular Expressions in R
      • 6.6 Regular Expressions
    • Lesson 7 - Course Summary

      • Course Summary
    • Lesson 1 Introduction

      • 1.1 Introduction
    • Lesson 2 Sample or population data

      • 2.1 Sample or population data
    • Lesson 3 The fundamentals of descriptive statistics

      • 3.1 The fundamentals of descriptive statistics
      • 3.2 Levels of measurement
      • 3.3 Categorical variables. Visualization techniques for categorical variables
      • 3.4 Numerical variables. Using a frequency distribution table
      • 3.5 Histogram charts
      • 3.6 Cross tables and scatter plots
    • Lesson 4 Measures of central tendency, asymmetry, and variability

      • 4.1 Measures of central tendency, asymmetry, and variability
      • 4.2 Measuring skewness
      • 4.3 Measuring how data is spread out calculating variance
      • 4.4 Standard deviation and coefficient of variation
      • 4.5 Calculating and understanding covariance
      • 4.6 The correlation coefficient
    • Lesson 5 Practical example descriptive statistics

      • 5.1 Practical example descriptive statistics
    • Lesson 6 Distributions

      • 6.1 Distributions
      • 6.2 What is a distribution
      • 6.3 The Normal distribution
      • 6.4 The standard normal distribution
      • 6.5 Understanding the central limit theorem
      • 6.6 Standard error
    • Lesson 7 Estimators and Estimates

      • 7.1 Estimators and Estimates
      • 7.2 Confidence intervals - an invaluable tool for decision making
      • 7.3 Calculating confidence intervals within a population with a known variance
      • 7.4 Student’s T distribution
      • 7.5 Calculating confidence intervals within a population with an unknown variance
      • 7.6 What is a margin of error and why is it important in Statistics
    • Lesson 8 Confidence intervals advanced topics

      • 8.1 Confidence intervals advanced topics
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
    • Lesson 9 Practical example inferential statistics

      • 9.1 Practical example inferential statistics
    • Lesson 10 Hypothesis testing Introduction

      • 10.1 Hypothesis testing Introduction
      • 10.2 Establishing a rejection region and a significance level
      • 10.3 Type I error vs Type II error
    • Lesson 11 Hypothesis testing Let's start testing!

      • 11.1 Hypothesis testing Let's start testing!
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
      • 11.3 Test for the mean. Population variance unknown
      • 11.4 Test for the mean. Dependent samples
      • 11.5 Test for the mean. Independent samples (Part One)
      • 11.6 Test for the mean. Independent samples (Part Two)
    • Lesson 12 Practical example hypothesis testing

      • 12.1 Practical example hypothesis testing
    • Lesson 13 The fundamentals of regression analysis

      • 13.1 The fundamentals of regression analysis
      • 13.2 Correlation and causation
      • 13.3 The linear regression model made easy
      • 13.4 What is the difference between correlation and regression
      • 13.5 A geometrical representation of the linear regression model
      • 13.6 A practical example - Reinforced learning
    • Lesson 14 Subtleties of regression analysis

      • 14.1 Subtleties of regression analysis
      • 14.2 What is Rsquared and how does it help us
      • 14.3 The ordinary least squares setting and its practical applications
      • 14.4 Studying regression tables
      • 14.5 The multiple linear regression model
      • 14.6 Adjusted R-squared
      • 14.7 What does the F-statistic show us and why we need to understand it
    • Lesson 15 Assumptions for linear regression analysis

      • 15.1 Assumptions for linear regression analysis
      • 15.2 Linearity
      • 15.3 No endogeneity
      • 15.4 Normality and homoscedasticity
      • 15.5 No autocorrelation
      • 15.6 No multicollinearity
    • Lesson 16 Dealing with categorical data

      • 16.1 Dealing with categorical data
    • Lesson 17 Practical example regression analysis

      • 17.1 Practical example regression analysis
    • Lesson 1 Welcome

      • Learning Objectives
      • Welcome
    • Lesson 2 Basic Visualization Tools

      • Learning Objectives
      • Bar Charts
      • Histograms
      • Pie Charts
      • Basic Visualization Tools
    • Lesson 3 Basic Visualization Tools Continued

      • Learning Objectives
      • Scatter Plots
      • Line Plots and Regression
      • Basic Visualization Tools (Continued)
    • Lesson 4 Specialized Visualization Tools

      • Learning Objectives
      • Word Clouds
      • Radar Charts
      • Waffle Charts
      • Box Plots
      • Word Cloud
      • Radar Charts
      • Waffle Charts
      • Box Plots
    • Lesson 5 How to Create Maps

      • Learning Objectives
      • Creating Maps in R
      • Maps
    • Lesson 6 How-to-build interactive Webpages

      • Learning Objectives
      • Introduction To Shiny
      • Creating and Customizing Shiny Apps
      • Additional Shiny Features
    • Lesson 7 Course Summary

      • Course Summary
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Exam & certification FREE PRACTICE TEST

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:
    • Attend one complete batch.
    • Complete 1 project
    Online Self-Learning:
    • Complete 85% of the course.
    • Complete 1 project

  • Who provides the certification?

    After successful completion of the Data Science - R Programming training, you will be awarded the course completion certificate from Simplilearn.

  • Is this course accredited?

    No, this course is not officially accredited.

  • How do I pass the Data Science - R Programming course?

    To pass the Data Science - R Programming course, you must: 

    • Complete 85% of the data science course
    • Complete any one project out of the four provided in the course. You will submit the project deliverables in the LMS, which will be evaluated by our lead trainer
    • Score a minimum of 60% in any one of the two simulation tests
    • Pass the online exam with a minimum score of 80%.

  • How long does it take to complete the Data Science course?

    It will take about 40 hours to complete the certification course successfully.

  • How many attempts do I have to pass the Data Science - R Programming course exam?

    You have a maximum of three attempts to pass the Data Science - R Programming certification exam. Simplilearn provides guidance and support for learners to help them pass the exam. 

  • How long is the Data Science - R Programming course certificate from Simplilearn valid for?

    The Data Science - R Programming course certification from Simplilearn has lifelong validity.

  • If I pass the Data Science - R Programming certification course exam, when and how do I receive my certificate?

    Upon successful completion of the course and passing the exam, you will receive the certificate through our Learning Management System which you can download or share via email or Linkedin.

  • Do you offer a money back guarantee?

    Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.

  • If I fail the Data Science - R Programming exam how soon can I retake it?

    You can re-attempt it immediately.

Course advisor

Ronald van Loon
Ronald van Loon Top 10 Big Data and Data Science Influencer, Director - Advertisement

Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author of a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.

Simon Tavasoli
Simon Tavasoli Analytics Lead at Cancer Care Ontario

Simon is a Data Scientist with 12 years of experience in healthcare analytics. He has a Master’s in Biostatistics from the University of Western Ontario. Simon is passionate about teaching data science and has a number of journal publications in preventive medicine analytics.


Anirudh Kunte
Anirudh Kunte Business Analyst - Global Consulting Practice at TCS, Mumbai

The in-depth coverage of the foundation concepts by the trainer is really enriching.

Hemant Tupalli
Hemant Tupalli Associate Director Clinical Team Management - Asia Pacific at PRA International, Mumbai

The overall experience has been good. I want to take this opportunity to specifically thank the trainer, who has been absolutely brilliant. The trainer is taking the effort to address the issue of having people from various backgrounds. I am looking forward to recommend this course to my colleagues.

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Sachin Shelar
Sachin Shelar Project Lead at Capgemini, Mumbai

I took the Data Science Certification Training - R Programming from Simplilearn, and it's amazing. It certainly added value to my career. The trainer conducted the sessions very nicely and made the learning easy and interesting. Great job Simplilearn!

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Ninad Sonkavde
Ninad Sonkavde Project Manager at netBlade Solutions Pvt. Ltd., Mumbai

The trainer is an extremely knowledgeable and passionate person, and is probably one of the BEST trainers Simplilearn has. He goes a the extra mile to make everyone understand the basics of every topic.

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Rohit Kumar
Rohit Kumar Consultant, Delhi

I really loved the way Shubham elaborates the concepts, how he starts from the basics and then gradually picks up the pace.

Dhanya Sasidharan
Dhanya Sasidharan Bangalore

I believe that Simplilearn is one of the best online platforms for learning. I completed my Data Scientist course from Simplilearn and had a wonderful experience. The technical support was really great and I could get my labs up and running in a very short span of time. The course content was also really good, covering in-depth and also the projects, where one could easily apply the concepts learned.

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Lavanya Krishnan
Lavanya Krishnan RePM consultant, Bangalore

My instructor Shilesh gave me a lot of hands-on training and made us use the R-platform in ways that were practical and useful. It was indeed a good course.

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Puneeta C.
Puneeta C. Student at Rajasthan Technical University, Bangalore

Simplilearn is the best platform to provide Certification Courses on Data Scientist, and it's Projects and Assignments. They are amazing. Keep Learning. Thank You

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Sabyasachi Guharoy
Sabyasachi Guharoy Solution Architect - Testing at Capgemini Technology Services India Pvt., Bangalore

I enrolled in Simplilearn for an Online Self Learning course on Data Science Certification Training - R Programming. The LMS interface is very user-friendly and the course material is lucid and easy to understand. I have enjoyed my learning experience with Simplilearn

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Amol B
Amol B Associate Manager at Firepro Systems, Bangalore

Simplilearn is the awesome learning platform. The courses are very well designed and the live classes have personal attention in terms resolving the doubts. Thanks Simplilearn.

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Shreya Sinha
Shreya Sinha Business Development Associate at BYJU'S, Delhi

Simplilearn has been fantastic when it comes to giving professional training. Everybody suggested me not to go for R programming online as it becomes difficult to learn such a tough course online. But to my surprise, the content and the trainers at Simplilearn made my learning experience so smooth and efficient that I was bound to recommend it to others. Go ahead without any hesitation. It will pay off.

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Ashish Ranjan
Ashish Ranjan Data Scientist at Accenture, Pune

Simplilearn is a good platform for starting the data science knowledge. Data Science with R course has helped me to get a rise from a Business Analyst to Data Scientist.

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Ajeya Kumar
Ajeya Kumar Associate Director at IHS Markit, Bangalore

The trainer is excellent. Real-time experiences shared during training are very helpful. Overall I am very happy with the training.

Manish Beniwal
Manish Beniwal Advisor Reporting - Global Mobility at Rio Tinto, Bangalore

I am Data Analyst with 7 years of work experience, but I didn't have the chance to work with Statistics like I am in this course. Its a good course even for beginners. Overall, the training is very good. Thank you Simplilearn.

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Debashis Sen
Debashis Sen Researcher at S&P Capital IQ, Bangalore

The course material of the Data Science program was well designed for beginners. The presentations were precise and to the point. The mentors in the various sessions were helpful and kept close to the basics. The examples used mirror real life scenarios, hence are very useful. Finally, the members of the CD team were truly delightful!

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    • What are the System Requirements?

      You will need to download R from the CRAN website and RStudio for your operating system. These are both open source and the installation guidelines are presented in the data science course.

    • Who are our instructors and how are they selected?

      All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty for data science online training.

    • What training formats are used for this course?

      We offer this data science with R certification course in the following formats:

      Live Virtual Classroom or Online Classroom: With online classroom training, you have the option to attend the course remotely from your desktop via video conferencing. This format reduces productivity challenges and decreases your time spent away from work or home.

      Online Self-Learning: In this mode, you’ll receive lecture videos that you can view at your own pace.

    • What if I miss a class?

      We record the class sessions and provide them to participants after the session is conducted. If you miss a class, you can view the recording before the next class session.

    • Can I cancel my enrollment? Will I get a refund?

      Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

    • Who provides the certification?

      At the end of the training, subject to satisfactory evaluation of the project and passing the online exam (minimum 80%), you will receive a certificate from Simplilearn stating that you are a certified data scientist with R programming.

    • Are there any group discounts for classroom training programs?

      Yes, we offer group discounts for our online training programs. Get in touch with us over the Drop us a Query or Request a Callback or Live Chat channels to find out more about our group discount packages.

    • What payment options are available?

      Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.
      • Visa Credit or Debit Card
      • MasterCard
      • American Express
      • Diner’s Club
      • PayPal

    • I’d like to learn more about this training program. Whom should I contact?

      Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

    • What is the Expert Assistant Support provided by Simplilearn?

      Expert Assistance includes:
      • Mentoring Sessions: Live Interaction with a subject matter expert to help participants with queries regarding project implementation and the course in general
      • Guidance on forum: Industry experts to respond to participant queries regarding technical concepts, projects and case studies.

      Teaching Assistance includes:
      • Project Assistance: Queries related to solving and completing projects and case studies, which are part of the Data Scientist with R programming course
      • Technical Assistance: Queries related to technical, installation and administration issues in Data Scientist with R programming training. In cases of critical issues, support will be rendered through a remote desktop.
      • R Programming: Queries related to R programming while solving and completing projects and case studies

    • How do I contact support?

      Submit a request to Simplilearn through any of following channels: Help & Support, Simplitalk, or Live Chat. A teaching assistant will get in touch with you within 48 hours.

    • What is Global Teaching Assistance?

      Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

    • What is covered under the 24/7 Support promise?

      We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

    • What is online classroom training?

      Online classroom training for Data Science Certification is conducted via online live streaming of each class. The classes are conducted by a Data Science certified trainer with more than 15 years of work and training experience.

    • Is this live training, or will I watch pre-recorded videos?

      If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the online classroom Flexi Pass, you will have access to live training conducted online as well as the pre-recorded videos.

    • Are the training and course material effective in preparing me for the Data Science - R Programming certification exam?

      Yes, Simplilearn’s training and course materials guarantee success with the Data Science - R Programming certification exam.

    • What certification will I receive after completing the training?

      After successful completion of the Data Science - R Programming Certification training, you will be awarded the course completion certificate from Simplilearn.

    • * Disclaimer

      * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

    Our Mumbai Correspondence / Mailing address

    Simplilearn Solutions Pvt Ltd, 601, 6th Floor, Rupa Solitaire, Millennium Business Park, Plot No.A-1, Mahape, Navi Mumbai - 400710, Maharashtra, India, Call us at: 1800-102-9602

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.