Data Science with R Course Overview

The Data Science with R programming certification training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

R Certification Training Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this R Programming course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 64 hours of Applied Learning
  • 10 real-life industry projects
  • Dedicated mentoring session from industry experts
  • Lifetime access to self-paced learning

Skills Covered

  • Business analytics
  • R programming and its packages
  • Data structures and data visualization
  • Apply functions and DPLYR function
  • Graphics in R for data visualization
  • Hypothesis testing
  • Apriori algorithm
  • kmeans and DBSCAN clustering

Benefits

The Big Data Analytics market is expected to reach $40.6 billion by 2023, at a growth rate of 29.7-percent. Randstad reports that pay hikes in the analytics industry are 50-percent higher than the IT industry. Learning R can help you begin a career in data science.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    $43KMin
    $62KAverage
    $95KMax
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Data Analyst professionals in Hong Kong
    JPMorgan Chase hiring for Data Analyst professionals in Hong Kong
    Genpact hiring for Data Analyst professionals in Hong Kong
    VMware hiring for Data Analyst professionals in Hong Kong
    LarsenAndTurbo hiring for Data Analyst professionals in Hong Kong
    Citi hiring for Data Analyst professionals in Hong Kong
    Accenture hiring for Data Analyst professionals in Hong Kong
    Source: Indeed
  • Annual Salary
    $83KMin
    $113KAverage
    $154KMax
    Source: Glassdoor
    Hiring Companies
    Accenture hiring for Data Scientist professionals in Hong Kong
    Oracle hiring for Data Scientist professionals in Hong Kong
    Microsoft hiring for Data Scientist professionals in Hong Kong
    Walmart hiring for Data Scientist professionals in Hong Kong
    Amazon hiring for Data Scientist professionals in Hong Kong
    Source: Indeed

Training Options

Self-Paced Learning

$ 699

  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 7 hands-on R projects to perfect the skills learned
  • Simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

online Bootcamp

$ 799

  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Classes starting in Hong Kong from:-
7th Aug: Weekend Class
14th Aug: Weekend Class
Show all classes

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

R Certification Course Curriculum

Eligibility

This Data Science with R certification training is beneficial for all aspiring data scientists including, IT professionals or software developers looking to make a career switch into Data analytics, professionals working in data and business analysis, graduates wishing to build a career in Data Science, and experienced professionals willing to harness Data Science in their fields.
Read More

Pre-requisites

There are no prerequisites for this Data Science with R certification course. If you are a beginner in Data Science, this is one of the best courses to start with.
Read More

Course Content

  • Data Science with R Programming

    Preview
    • Lesson 00 - Course Introduction

      01:31Preview
      • Course Introduction
        01:31
    • Lesson 01 - Introduction to Business Analytics

      21:06Preview
      • 1.001 Overview
        00:44
      • 1.002 Business Decisions and Analytics
        04:33
      • 1.003 Types of Business Analytics
        03:53
      • 1.004 Applications of Business Analytics
        08:57
      • 1.005 Data Science Overview
        01:29
      • 1.006 Conclusion
        01:30
      • Knowledge Check
    • Lesson 02 - Introduction to R Programming

      26:35Preview
      • 2.001 Overview
        00:31
      • 2.002 Importance of R
        05:20
      • 2.003 Data Types and Variables in R
        02:14
      • 2.004 Operators in R
        04:39
      • 2.005 Conditional Statements in R
        02:45
      • 2.006 Loops in R
        05:07
      • 2.007 R script
        01:44
      • 2.008 Functions in R
        02:58
      • 2.009 Conclusion
        01:17
      • Knowledge Check
    • Lesson 03 - Data Structures

      50:57Preview
      • 3.001 Overview
        01:04
      • 3.002 Identifying Data Structures
        13:14
      • 3.003 Demo Identifying Data Structures
        14:05
      • 3.004 Assigning Values to Data Structures
        04:51
      • 3.005 Data Manipulation
        09:23
      • 3.006 Demo Assigning values and applying functions
        07:46
      • 3.007 Conclusion
        00:34
      • Knowledge Check
    • Lesson 04 - Data Visualization

      29:40Preview
      • 4.001 Overview
        00:29
      • 4.002 Introduction to Data Visualization
        03:03
      • 4.003 Data Visualization using Graphics in R
        18:50
      • 4.004 ggplot2
        05:14
      • 4.005 File Formats of Graphic Outputs
        01:08
      • 4.006 Conclusion
        00:56
      • Knowledge Check
    • Lesson 05 - Statistics for Data Science-I

      14:10Preview
      • 5.001 Overview
        00:21
      • 5.002 Introduction to Hypothesis
        02:06
      • 5.003 Types of Hypothesis
        03:13
      • 5.004 Data Sampling
        02:48
      • 5.005 Confidence and Significance Levels
        04:33
      • 5.006 Conclusion
        01:09
      • Knowledge Check
    • Lesson 06 - Statistics for Data Science-II

      29:55Preview
      • 6.001 Overview
        00:28
      • 6.002 Hypothesis Test
        00:47
      • 6.003 Parametric Test
        14:36
      • 6.004 Non-Parametric Test
        08:31
      • 6.005 Hypothesis Tests about Population Means
        02:09
      • 6.006 Hypothesis Tests about Population Variance
        00:45
      • 6.007 Hypothesis Tests about Population Proportions
        01:11
      • 6.008 Conclusion
        01:28
      • Knowledge Check
    • Lesson 07 - Regression Analysis

      45:04Preview
      • 7.001 Overview
        00:26
      • 7.002 Introduction to Regression Analysis
        01:11
      • 7.003 Types of Regression Analysis Models
        01:38
      • 7.004 Linear Regression
        08:59
      • 7.005 Demo Simple Linear Regression
        07:29
      • 7.006 Non-Linear Regression
        03:49
      • 7.007 Demo Regression Analysis with Multiple Variables
        13:29
      • 7.008 Cross Validation
        01:48
      • 7.009 Non-Linear to Linear Models
        02:06
      • 7.010 Principal Component Analysis
        02:45
      • 7.011 Factor Analysis
        00:26
      • 7.012 Conclusion
        00:58
      • Knowledge Check
    • Lesson 08 - Classification

      01:05:14Preview
      • 8.001 Overview
        00:31
      • 8.002 Classification and Its Types
        04:24
      • 8.003 Logistic Regression
        03:35
      • 8.004 Support Vector Machines
        04:26
      • 8.005 Demo Support Vector Machines
        11:13
      • 8.006 K-Nearest Neighbours
        02:34
      • 8.007 Naive Bayes Classifier
        02:53
      • 8.008 Demo Naive Bayes Classifier
        06:15
      • 8.009 Decision Tree Classification
        09:47
      • 8.010 Demo Decision Tree Classification
        06:25
      • 8.011 Random Forest Classification
        02:01
      • 8.012 Evaluating Classifier Models
        06:04
      • 8.013 Demo K-Fold Cross Validation
        04:09
      • 8.014 Conclusion
        00:57
      • Knowledge Check
    • Lesson 09 - Clustering

      28:10Preview
      • 9.001 Overview
        00:17
      • 9.002 Introduction to Clustering
        02:57
      • 9.003 Clustering Methods
        07:47
      • 9.004 Demo K-means Clustering
        11:15
      • 9.005 Demo Hierarchical Clustering
        05:02
      • 9.006 Conclusion
        00:52
      • Knowledge Check
    • Lesson 10 - Association

      23:13Preview
      • 10.001 Overview
        00:15
      • 10.002 Association Rule
        06:20
      • 10.003 Apriori Algorithm
        05:19
      • 10.004 Demo Apriori Algorithm
        10:37
      • 10.005 Conclusion
        00:42
      • Knowledge Check
  • Free Course
  • Math Refresher

    Preview
    • Math Refresher

      30:35Preview
      • Math Refresher
        30:35
  • Free Course
  • Statistics Essential for Data Science

    Preview
    • Lesson 01: Course Introduction

      07:05Preview
      • 1.01 Course Introduction
        05:19
      • 1.02 What Will You Learn
        01:46
    • Lesson 02: Introduction to Statistics

      18:41Preview
      • 2.01 Learning Objectives
        01:16
      • 2.02 What Is Statistics
        01:50
      • 2.03 Why Statistics
        02:06
      • 2.04 Difference between Population and Sample
        01:21
      • 2.05 Different Types of Statistics
        02:42
      • 2.06 Importance of Statistical Concepts in Data Science
        03:20
      • 2.07 Application of Statistical Concepts in Business
        02:11
      • 2.08 Case Studies of Statistics Usage in Business
        03:09
      • 2.09 Recap
        00:46
    • Lesson 03: Understanding the Data

      17:29Preview
      • 3.01 Learning Objectives
        01:12
      • 3.02 Types of Data in Business Contexts
        02:11
      • 3.03 Data Categorization and Types of Data
        03:13
      • 3.03 Types of Data Collection
        02:14
      • 3.04 Types of Data
        02:01
      • 3.05 Structured vs. Unstructured Data
        01:46
      • 3.06 Sources of Data
        02:17
      • 3.07 Data Quality Issues
        01:38
      • 3.08 Recap
        00:57
    • Lesson 04: Descriptive Statistics

      32:03Preview
      • 4.01 Learning Objectives
        01:26
      • 4.02 Mathematical and Positional Averages
        03:15
      • 4.03 Measures of Central Tendancy: Part A
        02:17
      • 4.04 Measures of Central Tendancy: Part B
        02:41
      • 4.05 Measures of Dispersion
        01:15
      • 4.06 Range Outliers Quartiles Deviation
        02:30
      • 4.07 Mean Absolute Deviation (MAD) Standard Deviation Variance
        02:52
      • 4.08 Z Score and Empirical Rule
        02:14
      • 4.09 Coefficient of Variation and Its Application
        02:06
      • 4.10 Measures of Shape
        02:39
      • 4.11 Summarizing Data
        02:03
      • 4.12 Recap
        00:54
      • 4.13 Case Study One: Descriptive Statistics
        05:51
    • Lesson 05: Data Visualization

      20:55Preview
      • 5.01 Learning Objectives
        00:57
      • 5.02 Data Visualization
        02:15
      • 5.03 Basic Charts
        01:52
      • 5.04 Advanced Charts
        02:19
      • 5.05 Interpretation of the Charts
        02:57
      • 5.06 Selecting the Appropriate Chart
        02:25
      • 5.07 Charts Do's and Dont's
        02:47
      • 5.08 Story Telling With Charts
        01:29
      • 5.09 Recap
        00:50
      • 5.10 Case Study Two: Data Visualization
        03:04
    • Lesson 06: Probability

      19:46Preview
      • 6.01 Learning Objectives
        00:55
      • 6.02 Introduction to Probability
        03:10
      • 6.03 Key Terms in Probability
        02:25
      • 6.04 Conditional Probability
        02:11
      • 6.05 Types of Events: Independent and Dependent
        02:56
      • 6.06 Addition Theorem of Probability
        01:58
      • 6.07 Multiplication Theorem of Probability
        02:08
      • 6.08 Bayes Theorem
        03:10
      • 6.09 Recap
        00:53
    • Lesson 07: Probability Distributions

      22:29Preview
      • 7.01 Learning Objectives
        00:52
      • 7.02 Random Variable
        02:21
      • 7.03 Probability Distributions Discrete vs.Continuous: Part A
        01:44
      • 7.04 Probability Distributions Discrete vs.Continuous: Part B
        01:45
      • 7.05 Commonly Used Discrete Probability Distributions: Part A
        03:18
      • 7.06 Discrete Probability Distributions: Poisson
        03:16
      • 7.07 Binomial by Poisson Theorem
        01:37
      • 7.08 Commonly Used Continuous Probability Distribution
        03:22
      • 7.09 Applicaton of Normal Distribution
        02:49
      • 7.10 Recap
        01:25
    • Lesson 08: Sampling and Sampling Techniques

      30:53Preview
      • 8.01 Learnning Objectives
        00:51
      • 8.02 Introduction to Sampling and Sampling Errors
        03:05
      • 8.03 Advantages and Disadvantages of Sampling
        01:31
      • 8.04 Probability Sampling Methods: Part A
        02:32
      • 8.05 Probability Sampling Methods: Part B
        02:27
      • 8.06 Non-Probability Sampling Methods: Part A
        01:42
      • 8.07 Non-Probability Sampling Methods: Part B
        01:25
      • 8.08 Uses of Probability Sampling and Non-Probability Sampling
        02:08
      • 8.09 Sampling
        01:08
      • 8.10 Probability Distribution
        02:53
      • 8.11 Theorem Five Point One
        00:52
      • 8.12 Center Limit Theorem
        02:14
      • 8.13 Recap
        01:07
      • 8.14 Case Study Three: Sample and Sampling Techniques
        05:16
      • 8.15 Spotlight
        01:42
    • Lesson 09: Inferential Statistics

      33:59Preview
      • 9.01 Learning Objectives
        01:04
      • 9.02 Hypothesis and Hypothesis Testing in Businesses
        03:24
      • 9.03 Null and Alternate Hypothesis
        01:44
      • 9.04 P Value
        03:22
      • 9.05 Levels of Significance
        01:16
      • 9.06 Type One and Two Errors
        01:37
      • 9.07 Z Test
        02:24
      • 9.08 Confidence Intervals and Percentage Significance Level: Part A
        02:52
      • 9.09 Confidence Intervals: Part B
        01:20
      • 9.10 One Tail and Two Tail Tests
        04:43
      • 9.11 Notes to Remember for Null Hypothesis
        01:02
      • 9.12 Alternate Hypothesis
        01:51
      • 9.13 Recap
        00:56
      • 9.14 Case Study Four: Inferential Statistics
        06:24
      • Hypothesis Testing
    • Lesson 10: Application of Inferential Statistics

      27:07Preview
      • 10.01 Learning Objectives
        00:50
      • 10.02 Bivariate Analysis
        02:01
      • 10.03 Selecting the Appropriate Test for EDA
        02:29
      • 10.04 Parametric vs. Non-Parametric Tests
        01:54
      • 10.05 Test of Significance
        01:38
      • 10.06 Z Test
        04:14
      • 10.07 T Test
        00:54
      • 10.08 Parametric Tests ANOVA
        03:26
      • 10.09 Chi-Square Test
        02:31
      • 10.10 Sign Test
        01:58
      • 10.11 Kruskal Wallis Test
        01:04
      • 10.12 Mann Whitney Wilcoxon Test
        01:18
      • 10.13 Run Test for Randomness
        01:53
      • 10.14 Recap
        00:57
    • Lesson 11: Relation between Variables

      18:08Preview
      • 11.01 Learning Objectives
        01:06
      • 11.02 Correlation
        01:54
      • 11.03 Karl Pearson's Coefficient of Correlation
        02:36
      • 11.04 Karl Pearsons: Use Cases
        01:30
      • 11.05 Spearmans Rank Correlation Coefficient
        02:14
      • 11.06 Causation
        01:47
      • 11.07 Example of Regression
        02:28
      • 11.08 Coefficient of Determination
        01:12
      • 11.09 Quantifying Quality
        02:29
      • 11.10 Recap
        00:52
    • Lesson 12: Application of Statistics in Business

      17:25Preview
      • 12.01 Learning Objectives
        00:53
      • 12.02 How to Use Statistics In Day to Day Business
        03:29
      • 12.03 Example: How to Not Lie With Statistics
        02:34
      • 12.04 How to Not Lie With Statistics
        01:49
      • 12.05 Lying Through Visualizations
        02:15
      • 12.06 Lying About Relationships
        03:31
      • 12.07 Recap
        01:06
      • 12.08 Spotlight
        01:48
    • Lesson 13: Assisted Practice

      11:47Preview
      • Assisted Practice: Problem Statement
        02:10
      • Assisted Practice: Solution
        09:37

Industry Project

  • Project 1

    Products rating prediction for Amazon

    Help Amazon, a US-based e-commerce company, improve its recommendation engine by predicting ratings for the non-rated products and adding them to recommendations accordingly.

    Products rating prediction for Amazon
  • Project 2

    Demand Forecasting for Walmart

    Predict accurate sales for 45 Walmart stores, considering the impact of promotional markdown events. Check if macroeconomic factors have an impact on sales.

    Demand Forecasting for Walmart
  • Project 3

    Improving customer experience for Comcast

    Provide Comcast, a US-based global telecom company, key recommendations to improve customer experience by identifying and improving problem areas that lower customer satisfaction.

    Improving customer experience for Comcast
  • Project 4

    Attrition Analysis for IBM

    IBM, a leading US-based IT company, wants to identify the factors that influence employee attrition by building a logistics regression model that can help predict employee churn.

    Attrition Analysis for IBM
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Data Science with R Course Advisor

  • Ronald van Loon

    Ronald van Loon

    Top 10 Big Data and Data Science Influencer, Director - Adversitement

    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.

prevNext

Data Science with R Exam & Certification

Data Science with R Programming Training in Hong Kong
  • Who provides the certification?

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

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

    Online Classroom:
    • Attend one complete batch of Data Science with R certification training
    • Complete 1 project
    Online Self-Learning:
    • Complete 85% of the course.
    • Complete 1 project

  • How long is the Data Science with R certificate from Simplilearn valid for?

    The Data Science with R certificate from Simplilearn has lifelong validity.

  • How long does it take to complete the course?

    It will take about 40 hours to complete the R programming online course successfully.

  • How many attempts do I have to pass the Data Science with R certification exam?

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

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

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

  • If I fail the Data Science with R certification exam how soon can I retake it?

    You can re-attempt it immediately.

  • Do you provide any practice tests as part of Data Science with R course?

    Yes, we provide 1 practice test as part of our Data Science with R course to help you prepare for the actual certification exam. You can try this free R Programming practice questions to understand the type of tests that are part of the course curriculum.

  • Is this R course accredited?

    No, this R course is not officially accredited.

R Certification Training Reviews

  • Savish Dan

    Savish Dan

    New York City

    The course helped me to improve my skill set and gain the confidence to handle the role of an analyst. I had a break in my career due to immigration policies and had utilized the time to learn new skills, which helped me get a new job faster.

  • Yune Leou-On

    Yune Leou-On

    Market Research and Monetization | Peanut Labs, Houston

    I have taken Simplilearn's Data Science course & will now be taking their CAPM program. This has helped me professionally and academically, & I recommend them to anyone.

  • Rodney Swann

    Rodney Swann

    Senior Facility Manager at CBRE, Houston

    Excellent instructor with the ability to provide real world experience and insights. Emphasis on the tools along with practical and useful insights. It is not an easy course for those without programming experience, but it does take away some of the mystery and confusion associated with using these tools.

  • Saad Madaha

    Saad Madaha

    Programmer Analyst III - Cardiology Information Systems at New York-Presbyterian Hospital, New York City

    Level of granularity. Tutor knowledge. Class size. Tutor's confidence, subject knowledge, and high level of commitment to student understanding of the material. Tutor assisted students who had issues with SAS installation. Great Tutor-Student interaction.

  • Rodney Swann

    Rodney Swann

    Senior Facility Manager at CBRE, Houston

    My instructor is obviously a Pro at what she does. I wish I had someone around like her to mentor me when I was younger. Some of the technical aspects of the course are a little challenging, but the concepts for doing what is being taught is becoming clear to me. I hope this will make all the difference as I delve into the coursework even more.

  • Sasa Stevanovic

    Sasa Stevanovic

    Member of the Network on Institutional Investors and Long-term Investment, Houston

    Great experience with the provider, enjoyed learning, very helpful application, and staff support. Good start for mastering R, SAS, and Excel.

  • Ishanie Niyogi

    Ishanie Niyogi

    Associate at ICF, Atlanta

    The trainer was extremely knowledgeable about the course content and provided in-depth explanations for all questions that were asked. Thanks to the Simplilearn team for all the support provided during the training process.

  • Anubhav Ingole

    Anubhav Ingole

    Project Management Officer (PMO)/Business Analyst, Nagpur

    My instructor, Rajneesh, made the class very interactive. He explained each topic with real-life examples and analogies. I sincerely thank him for the effort he is putting into making a difference.

  • Sheetal Nagpal

    Sheetal Nagpal

    Delhi

    My trainer was very engaging and knowledgeable. I liked her way of teaching - sharing notes and providing hands-on practicals within the R training sessions. She also shared real-life examples, and a SWOT analysis during the stat test, which I thought was a fantastic idea. Project mentoring cleared all our doubts related to the project.

  • Amol B

    Amol B

    Bangalore

    Simplilearn has designed the R programming course in a systematic manner. It has its own UI to code the programs. In fact, the algorithm and its applications have been done in the most logical way. Thanks, Simplilearn

  • Rohit Kumar

    Rohit Kumar

    Consultant, Delhi

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

  • 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.

  • Farhan Nizar

    Farhan Nizar

    Lead Technical Analyst at NBAD, Abu Dhabi

    Course is designed in compliance with the data scientist job market, especially the Analytics concepts covered in this module will really help to correlate with the business scenarios and fulfill business needs. I am thankful to Simplilearn and trainers.

  • Amani Alawneh

    Amani Alawneh

    Head of Project Management, Geneve

    The course was delivered successfully. It was very punctual, organized, and thorough. Overall, it was good.

  • Marwa Abdalla

    Marwa Abdalla

    Technical Support Engineer at MDS TS, Abu Dhabi

    Simplilearn's Data Science certification training was a good experience. The trainer is great and the content of the course is valuable. Thank you Simplilearn.

prevNext

Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

R Certification Training FAQs

  • What is R programming?

    R is a programming language and free software developed in 1993, made up of a collection of libraries architectured especially for data science. As a tool, R is considered to be clear and accessible.

  • Why should I learn R programming?

    Data Science is one of the popular career domains among professionals that offers high earning potential. It mostly comprises statistics and R is the bridging language of this domain and is widely used for data analysis. By learning R programming, you can enter the world of business analytics and data visualization. It is a must-have skill for all those aspiring to become a Data Scientist.

  • How do beginners learn R online?

    Anyone who is looking to get started in IT or willing to further their IT career should consider learning R. We at Simplilearn have compiled an extensive content for Data Science beginners, along with supporting blogs and YouTube videos to help you understand the Data Science basics and importance of R in the dynamic field of data science.

  • Should I learn R or Python programming for a Data Science career?

    R and Python are the top languages that professionals learn to start a career in Data Science. Both languages are powerful and have their own pros and cons. So, depending on which language is used for data science projects in your organization and what can help you in the long run, you can make a choice.

    Simplilearn also provides Data Science with Python course which builds a strong foundation in data science and imparts all the valuable skills that employers look for in a data scientist.

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

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

  • What is online classroom training?

    Online classroom training for R certification course 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.

  • 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 R course curriculum.

  • Who are our instructors and how are they selected?

    All of our highly qualified R course trainers are industry Data Science 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.

  • What training formats are used for this R course?

    We offer this Data Science with R training in the following formats:

    Live Virtual Classroom or Online Classroom: With online classroom training, you have the option to attend the R 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 R training 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.

  • 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.

  • How do I enroll for this Data Science with R certification training?

    You can enroll in this Data Science with R certification training on our website and make an online payment using any of the following options:

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

    Once payment is received you will automatically receive a payment receipt and access information via email.

  • I’d like to learn more about this Data Science with R course. 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 Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in R programming 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 R training with us.

  • *Disclaimer

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

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