Data Science with R Certification Course

10886 Ratings

57819 Learners

64 hours of Applied Learning

10 real-life industry projects

Dedicated mentoring session from industry experts

Lifetime access to self-paced learning

COURSE PREVIEW

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Data Science with R Programming
- Lesson 00 - Course Introduction 08:36
  - Course Introduction01:31
  - Accessing Practice Lab07:05
- Lesson 01 - Introduction to Business Analytics 21:06
  - 1.001 Overview00:44
  - 1.002 Business Decisions and Analytics04:33
  - 1.003 Types of Business Analytics03:53
  - 1.004 Applications of Business Analytics08:57
  - 1.005 Data Science Overview01:29
  - 1.006 Conclusion01:30
  - Knowledge Check
- Lesson 02 - Introduction to R Programming 26:35
  - 2.001 Overview00:31
  - 2.002 Importance of R05:20
  - 2.003 Data Types and Variables in R02:14
  - 2.004 Operators in R04:39
  - 2.005 Conditional Statements in R02:45
  - 2.006 Loops in R05:07
  - 2.007 R script01:44
  - 2.008 Functions in R02:58
  - 2.009 Conclusion01:17
  - Knowledge Check
- Lesson 03 - Data Structures 50:57
  - 3.001 Overview01:04
  - 3.002 Identifying Data Structures13:14
  - 3.003 Demo Identifying Data Structures14:05
  - 3.004 Assigning Values to Data Structures04:51
  - 3.005 Data Manipulation09:23
  - 3.006 Demo Assigning values and applying functions07:46
  - 3.007 Conclusion00:34
  - Knowledge Check
- Lesson 04 - Data Visualization 26:25
  - 4.001 Overview00:29
  - 4.002 Introduction to Data Visualization03:03
  - 4.003 Data Visualization using Graphics in R15:35
  - 4.004 ggplot205:14
  - 4.005 File Formats of Graphic Outputs01:08
  - 4.006 Conclusion00:56
  - Knowledge Check
- Lesson 05 - Statistics for Data Science-I 14:10
  - 5.001 Overview00:21
  - 5.002 Introduction to Hypothesis02:06
  - 5.003 Types of Hypothesis03:13
  - 5.004 Data Sampling02:48
  - 5.005 Confidence and Significance Levels04:33
  - 5.006 Conclusion01:09
  - Knowledge Check
- Lesson 06 - Statistics for Data Science-II 29:55
  - 6.001 Overview00:28
  - 6.002 Hypothesis Test00:47
  - 6.003 Parametric Test14:36
  - 6.004 Non-Parametric Test08:31
  - 6.005 Hypothesis Tests about Population Means02:09
  - 6.006 Hypothesis Tests about Population Variance00:45
  - 6.007 Hypothesis Tests about Population Proportions01:11
  - 6.008 Conclusion01:28
  - Knowledge Check
- Lesson 07 - Regression Analysis 45:04
  - 7.001 Overview00:26
  - 7.002 Introduction to Regression Analysis01:11
  - 7.003 Types of Regression Analysis Models01:38
  - 7.004 Linear Regression08:59
  - 7.005 Demo Simple Linear Regression07:29
  - 7.006 Non-Linear Regression03:49
  - 7.007 Demo Regression Analysis with Multiple Variables13:29
  - 7.008 Cross Validation01:48
  - 7.009 Non-Linear to Linear Models02:06
  - 7.010 Principal Component Analysis02:45
  - 7.011 Factor Analysis00:26
  - 7.012 Conclusion00:58
  - Knowledge Check
- Lesson 08 - Classification 1:05:14
  - 8.001 Overview00:31
  - 8.002 Classification and Its Types04:24
  - 8.003 Logistic Regression03:35
  - 8.004 Support Vector Machines04:26
  - 8.005 Demo Support Vector Machines11:13
  - 8.006 K-Nearest Neighbours02:34
  - 8.007 Naive Bayes Classifier02:53
  - 8.008 Demo Naive Bayes Classifier06:15
  - 8.009 Decision Tree Classification09:47
  - 8.010 Demo Decision Tree Classification06:25
  - 8.011 Random Forest Classification02:01
  - 8.012 Evaluating Classifier Models06:04
  - 8.013 Demo K-Fold Cross Validation04:09
  - 8.014 Conclusion00:57
  - Knowledge Check
- Lesson 09 - Clustering 28:10
  - 9.001 Overview00:17
  - 9.002 Introduction to Clustering02:57
  - 9.003 Clustering Methods07:47
  - 9.004 Demo K-means Clustering11:15
  - 9.005 Demo Hierarchical Clustering05:02
  - 9.006 Conclusion00:52
  - Knowledge Check
- Lesson 10 - Association 23:13
  - 10.001 Overview00:15
  - 10.002 Association Rule06:20
  - 10.003 Apriori Algorithm05:19
  - 10.004 Demo Apriori Algorithm10:37
  - 10.005 Conclusion00:42
  - Knowledge Check
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Math Refresher
- Lesson 01: Course Introduction 06:23
  - 1.01 About Simplilearn00:28
  - 1.02 Introduction to Mathematics01:18
  - 1.03 Types of Mathematics02:39
  - 1.04 Applications of Math in Data Industry01:17
  - 1.05 Learning Path00:25
  - 1.06 Course Components00:16
- Lesson 02: Probability and Statistics 32:38
  - 2.01 Learning Objectives00:29
  - 2.02 Basics of Statistics and Probability03:08
  - 2.03 Introduction to Descriptive Statistics02:12
  - 2.04 Measures of Central Tendencies04:50
  - 2.05 Measures of Asymmetry02:24
  - 2.06 Measures of Variability04:55
  - 2.07 Measures of Relationship05:22
  - 2.08 Introduction to Probability08:36
  - 2.09 Key Takeaways00:42
  - 2.10 Knowledge check
- Lesson 03: Coordinate Geometry 06:31
  - 3.01 Learning Objectives00:35
  - 3.02 Introduction to Coordinate Geometry03:16
  - 3.03 Coordinate Geometry Formulas01:51
  - 3.04 Key Takeaways00:49
  - 3.05 Knowledge Check
- Lesson 04: Linear Algebra 29:53
  - 4.01 Learning Objectives00:29
  - 4.02 Introduction to Linear Algebra03:21
  - 4.03 Forms of Linear Equation05:21
  - 4.04 Solving a Linear Equation05:21
  - 4.05 Introduction to Matrices02:05
  - 4.06 Matrix Operations07:07
  - 4.07 Introduction to Vectors01:00
  - 4.08 Types and Properties of Vectors01:52
  - 4.09 Vector Operations02:39
  - 4.10 Key Takeaways00:38
  - 4.11 Knowledge Check
- Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition 08:56
  - 5.01 Learning Objectives00:29
  - 5.02 Eigenvalues01:19
  - 5.03 Eigenvectors04:09
  - 5.04 Eigendecomposition02:21
  - 5.05 Key Takeaways00:38
  - 5.06 Knowledge Check
- Lesson 06: Introduction to Calculus 09:47
  - 6.01 Learning Objectives00:30
  - 6.02 Basics of Calculus01:20
  - 6.03 Differential Calculus03:01
  - 6.04 Differential Formulas01:01
  - 6.05 Integral Calculus02:33
  - 6.06 Integration Formulas00:47
  - 6.07 Key Takeaways00:35
  - 6.08 Knowledge Check
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Statistics Essential for Data Science
- Lesson 01: Course Introduction 07:05
  - 1.01 Course Introduction05:19
  - 1.02 What Will You Learn01:46
- Lesson 02: Introduction to Statistics 25:49
  - 2.01 Learning Objectives01:16
  - 2.02 What Is Statistics01:50
  - 2.03 Why Statistics02:06
  - 2.04 Difference between Population and Sample01:20
  - 2.05 Different Types of Statistics02:42
  - 2.06 Importance of Statistical Concepts in Data Science03:20
  - 2.07 Application of Statistical Concepts in Business02:11
  - 2.08 Case Studies of Statistics Usage in Business03:09
  - 2.09 Applications of Statistics in Business: Time Series Forecasting03:50
  - 2.10 Applications of Statistics in Business Sales Forecasting03:19
  - 2.11 Recap00:46
- Lesson 03: Understanding the Data 17:29
  - 3.01 Learning Objectives01:12
  - 3.02 Types of Data in Business Contexts02:11
  - 3.03 Data Categorization and Types of Data03:13
  - 3.03 Types of Data Collection02:14
  - 3.04 Types of Data02:01
  - 3.05 Structured vs. Unstructured Data01:46
  - 3.06 Sources of Data02:17
  - 3.07 Data Quality Issues01:38
  - 3.08 Recap00:57
- Lesson 04: Descriptive Statistics 34:51
  - 4.01 Learning Objectives01:26
  - 4.02 Descriptive Statistics02:03
  - 4.03 Mathematical and Positional Averages03:15
  - 4.04 Measures of Central Tendancy: Part A02:17
  - 4.05 Measures of Central Tendancy: Part B02:41
  - 4.06 Measures of Dispersion01:15
  - 4.07 Range Outliers Quartiles Deviation02:30
  - 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance03:37
  - 4.09 Z Score and Empirical Rule02:14
  - 4.10 Coefficient of Variation and Its Application02:06
  - 4.11 Measures of Shape02:39
  - 4.12 Summarizing Data02:03
  - 4.13 Recap00:54
  - 4.14 Case Study One: Descriptive Statistics05:51
- Lesson 05: Data Visualization 23:36
  - 5.01 Learning Objectives00:57
  - 5.02 Data Visualization02:15
  - 5.03 Basic Charts01:52
  - 5.04 Advanced Charts02:19
  - 5.05 Interpretation of the Charts02:57
  - 5.06 Selecting the Appropriate Chart02:25
  - 5.07 Charts Do's and Dont's02:47
  - 5.08 Story Telling With Charts01:29
  - 5.09 Data Visualization: Example02:41
  - 5.10 Recap00:50
  - 5.11 Case Study Two: Data Visualization03:04
- Lesson 06: Probability 21:51
  - 6.01 Learning Objectives00:55
  - 6.02 Introduction to Probability03:10
  - 6.03 Probability Example02:02
  - 6.04 Key Terms in Probability02:25
  - 6.05 Conditional Probability02:11
  - 6.06 Types of Events: Independent and Dependent02:59
  - 6.07 Addition Theorem of Probability01:58
  - 6.08 Multiplication Theorem of Probability02:08
  - 6.09 Bayes Theorem03:10
  - 6.10 Recap00:53
- Lesson 07: Probability Distributions 24:45
  - 7.01 Learning Objectives00:52
  - 7.02 Probability Distribution01:25
  - 7.03 Random Variable02:21
  - 7.04 Probability Distributions Discrete vs.Continuous: Part A01:44
  - 7.05 Probability Distributions Discrete vs.Continuous: Part B01:45
  - 7.06 Commonly Used Discrete Probability Distributions: Part A03:18
  - 7.07 Discrete Probability Distributions: Poisson03:16
  - 7.08 Binomial by Poisson Theorem02:28
  - 7.09 Commonly Used Continuous Probability Distribution03:22
  - 7.10 Application of Normal Distribution02:49
  - 7.11 Recap01:25
- Lesson 08: Sampling and Sampling Techniques 36:45
  - 8.01 Learnning Objectives00:51
  - 8.02 Introduction to Sampling and Sampling Errors03:05
  - 8.03 Advantages and Disadvantages of Sampling01:31
  - 8.04 Probability Sampling Methods: Part A02:32
  - 8.05 Probability Sampling Methods: Part B02:27
  - 8.06 Non-Probability Sampling Methods: Part A01:42
  - 8.07 Non-Probability Sampling Methods: Part B01:25
  - 8.08 Uses of Probability Sampling and Non-Probability Sampling02:08
  - 8.09 Sampling01:08
  - 8.10 Probability Distribution02:53
  - 8.11 Theorem Five Point One00:52
  - 8.12 Center Limit Theorem02:14
  - 8.13 Sampling Stratified: Sampling Example 04:35
  - 8.14 Probability Sampling: Example01:17
  - 8.15 Recap01:07
  - 8.16 Case Study Three: Sample and Sampling Techniques05:16
  - 8.17 Spotlight01:42
- Lesson 09: Inferential Statistics 37:08
  - 9.01 Learning Objectives01:04
  - 9.02 Inferential Statistics03:09
  - 9.03 Hypothesis and Hypothesis Testing in Businesses03:24
  - 9.04 Null and Alternate Hypothesis01:44
  - 9.05 P Value03:22
  - 9.06 Levels of Significance01:16
  - 9.07 Type One and Two Errors01:37
  - 9.08 Z Test02:24
  - 9.09 Confidence Intervals and Percentage Significance Level: Part A02:52
  - 9.10 Confidence Intervals: Part B01:20
  - 9.11 One Tail and Two Tail Tests04:43
  - 9.12 Notes to Remember for Null Hypothesis01:02
  - 9.13 Alternate Hypothesis01:51
  - 9.14 Recap00:56
  - 9.15 Case Study 4: Inferential Statistics06:24
  - Hypothesis Testing
- Lesson 10: Application of Inferential Statistics 27:20
  - 10.01 Learning Objectives00:50
  - 10.02 Bivariate Analysis02:01
  - 10.03 Selecting the Appropriate Test for EDA02:29
  - 10.04 Parametric vs. Non-Parametric Tests01:54
  - 10.05 Test of Significance01:38
  - 10.06 Z Test04:27
  - 10.07 T Test00:54
  - 10.08 Parametric Tests ANOVA03:26
  - 10.09 Chi-Square Test02:31
  - 10.10 Sign Test01:58
  - 10.11 Kruskal Wallis Test01:04
  - 10.12 Mann Whitney Wilcoxon Test01:18
  - 10.13 Run Test for Randomness01:53
  - 10.14 Recap00:57
- Lesson 11: Relation between Variables 20:07
  - 11.01 Learning Objectives01:06
  - 11.02 Correlation01:54
  - 11.03 Karl Pearson's Coefficient of Correlation02:36
  - 11.04 Karl Pearsons: Use Cases01:30
  - 11.05 Correlation Example01:59
  - 11.06 Spearmans Rank Correlation Coefficient02:14
  - 11.07 Causation01:47
  - 11.08 Example of Regression02:28
  - 11.09 Coefficient of Determination01:12
  - 11.10 Quantifying Quality02:29
  - 11.11 Recap00:52
- Lesson 12: Application of Statistics in Business 17:25
  - 12.01 Learning Objectives00:53
  - 12.02 How to Use Statistics In Day to Day Business03:29
  - 12.03 Example: How to Not Lie With Statistics02:34
  - 12.04 How to Not Lie With Statistics01:49
  - 12.05 Lying Through Visualizations02:15
  - 12.06 Lying About Relationships03:31
  - 12.07 Recap01:06
  - 12.08 Spotlight01:48
- Lesson 13: Assisted Practice 11:47
  - Assisted Practice: Problem Statement02:10
  - Assisted Practice: Solution09:37
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Description Exam & Certification FAQ's

COURSE CURRICULUM

Description

Why should I learn Data Science with R from Simplilearn?
- This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
- According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
- Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
- Randstad reports that pay hikes in the analytics industry are 50% higher than the IT industry
What are the course objectives?
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies.
- Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
- Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
What will you learn in this Data Science course?
This data science training course will enable you to:
- Gain a foundational understanding of business analytics
- Install R, R-studio, and workspace setup, and learn about the various R packages
- Master R programming and understand how various statements are executed in R
- Gain an in-depth understanding of data structure used in R and learn to import/export data in R
- Define, understand and use the various apply functions and DPYR functions
- Understand and use the various graphics in R for data visualization
- Gain a basic understanding of various statistical concepts
- Understand and use hypothesis testing method to drive business decisions
- Understand and use linear, non-linear regression models, and classification techniques for data analysis
- Learn and use the various association rules and Apriori algorithm
- Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
Who should take this online Data Science training course?
There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:
- IT professionals looking for a career switch into data science and analytics
- Software developers looking for a career switch into data science and analytics
- Professionals working in data and business analytics
- Graduates looking to build a career in analytics and data science
- Anyone with a genuine interest in the data science field
- Experienced professionals who would like to harness data science in their fields
Prerequisites: There are no prerequisites for this data science online training course. If you are new in the field of data science, this is the best course to start with.
What Data Science projects will you work on during this course?
The data science certification course includes ten real-life, industry-based projects. Successful evaluation of one of the following six projects is a part of the certification eligibility criteria.

Project 1: Products rating prediction for Amazon

Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

Domain: E-commerce

Project 2: Demand Forecasting for Walmart

Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

Domain: Retail

Project 3: Improving customer experience for Comcast

Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

Domain: Telecom

Project 4: Attrition Analysis for IBM

IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

Domain: Workforce Analytics

Project 5:
A nationwide survey of hospital costs conducted by the US Agency for Healthcare consists of hospital records of inpatient samples. The given data is restricted to the state of Wisconsin and relates to patients in the age group 0-17 years. The agency wants to analyze the data to research on the health care costs and their utilization.

Domain: Healthcare

Project 6:
The data gives the details of third party motor insurance claims in Sweden for the year 1977. In Sweden, all motor insurance companies apply identical risk arguments to classify customers, and thus their portfolios and their claims statistics can be combined. The data were compiled by a Swedish Committee on the Analysis of Risk Premium in Motor Insurance. The Committee was asked to look into the problem of analyzing the real influence on the claims of the risk arguments and to compare this structure with the actual tariff.

Domain: Insurance

Project 7:
A high-end fashion retail store is looking to expand its products. It wants to understand the market and find the current trends in the industry. It has a database of all products with attributes, such as style, material, season, and the sales of the products over a period of two months.

Domain: Retail

Project 8:
The web analytics team of www.datadb.com is interested to understand the web activities of the site, which are the sources used to access the website. They have a database that states the keywords of time in the page, source group, bounces, exits, unique page views, and visits.

Domain: Internet

Project 9:

An education department in the US needs to analyze the factors that influence the admission of a student into a college. Analyze the historical data and determine the key drivers.

Domain: Education

Project 10:

A UK-based online retail store has captured the sales data for different products for the period of one year (Nov 2016 to Dec 2017). The organization sells gifts primarily on the online platform. The customers who make a purchase consume directly for themselves. There are small businesses that buy in bulk and sell to other customers through the retail outlet channel. Find significant customers for the business who make high purchases of their favorite products.

Domain: E-commerce

The course also includes 4 more projects for you to practice.
Project 11:
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.

Domain: Music Industry

Project 12:
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.

Domain: Finance

Project 13:
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.

Domain: Unemployment

Project 14:
Flight delays are frequently experienced when flying from the Washington DC area to the New York City area. By using logistical regression, you’ll identify flights that are likely to be delayed. The provided dataset helps with a number of variables including airports and flight times.

Domain: Airline

Exam & Certification

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.

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 Data science with 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 these R programming certificate course trainers 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.
What is the recommended learning path after completing Data Science with R Programming certification course?

You can either enroll in our Data Scientist Course or if you are looking to get a University certificate, you can enroll in the Data Science Certification.
How do I become an R Programmer?

To become an R programmer, you need a degree in IT, Computer Science, or any allied field. After this, you can go for an R certification for more comprehensive knowledge.
What is R Programming used for?

R is a programming language that is used for statistical computing graphics for cleaning, analyzing, and presenting data. By seeking an R course, you’ll get the opportunity to learn about R and its applications in detail.
Is the R course difficult to learn?

This Data Science with R course is easy to learn. Anyone can pursue it, whether a fresh IT graduate or professionals like IT professionals, analytics professionals, and software developers.
Is R Programmer a good career option?

R programmers are best suited for industries dealing with Data Science projects based on statistical model implementation for data analysis. They are paid quite well in the industry with a salary starting at $85,000, which may reach $122,000 with only ten years of experience (PayScale.com). Across the world, professionals with Data Science with R certification are in high demand. Presently, there are nearly 1 million R-specific job vacancies globally, making it one of the leading career options. Given this, it will be highly beneficial for you if you seek an R certification along with a degree in Data Science.
How do beginners learn R?

Beginners can learn R by seeking comprehensive R training that will help them have a profound base in R and seek expert-level knowledge. Upon taking the training, they must consider working on R-based projects to gain relevant practical experience.
Is R certification worth it?
Seeking this R training is worth it as it will help you:
- Gain a basic understanding of business analytics
- Install R, RStudio, workspace setup, and seek knowledge on the different R packages
- Master R programming and understand how different statements are executed in R
- Gain a deeper understanding of data structure used in R and learn how to import/export data in R
- Define, comprehend and use the various apply functions and DPLYR functions
- Understand and use the different graphics in R for data visualization
- Gain a basic understanding of different statistical concepts
- Gain understanding and know the use of the hypothesis testing method to drive business decisions
- Understand and utilize linear and non-linear regression models, and classification techniques for data analysis
- Learn and use the various association rules with the Apriori algorithm
- Learn and use clustering methods including k-means, DBSCAN, and hierarchical clustering
What are the job roles available after getting an R certification?
After obtaining an R certification, you can seek job roles in the form of:
- R programmer
- Data Scientist
- Data Analyst
- Data Architect
- Data Visualization Analyst
- Geo Statistician
- Database Administrator
- Quantitative Analyst
What does an R Programmer do?

The primary responsibility of an R programmer is to use the R programming language for developing scripts that can be used for data manipulation, modeling, prediction, visualization, and reporting. By attaining an R certification, you will better understand the roles and responsibilities of R programmers.
What skills should an R Programmer know?
An R programmer should be skilled at:
- Statistics
- Machine learning
- Programming languages in addition to R
- Visualization
- Communication
With this Data Science with R certification, you will be able to acquire these skills and learn much more about R programming and have a flourishing career ahead of you.
What industries use R Programming most?

Industries that use R programming the most are academic, healthcare, consulting, government, insurance, finance, manufacturing, electronics, and technology-based industries. After seeking an R course, you can easily seek job opportunities in these top industries.
Which companies hire R Programmers?

Some of the top companies hiring professionals with R certification are Facebook, Google, American Express, HP, Infosys, IBM, Deloitte, Capgemini, Oracle, Twitter, and Uber.
What book do you suggest reading for R Programming?
While pursuing an R course, you can consider referring to the following top books for more detailed knowledge:
- An Introduction to Statistical Learning: with Applications in R by Springer
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham
- Discovering Statistics Using R by Andy Field R
- For Dummies by Andrie de Vries, Joris Meys
- The Art of R Programming: A Tour of Statistical Software Design by Norman Matloff
What is the pay scale of R Programmers across the world?

By seeking an R certification, professionals can earn an average salary of $96,481 in a year.

Also, maximize Your Earning Potential with Certifications that pay well. Find out which certifications can help you command higher salaries and enjoy financial stability.

Our Learners

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.

Savish Dan
I took the R, SAS and Excel Course for Data Analytics. I was out of the workforce for a few months and had a background in statistics but I needed to refresh some skills before applying for jobs. Overall, the course was very strong. I liked how it was straight to the point without any bells and whistles. It often focused on concepts and the broader picture of learning. It spanned in complexity so one can kind push themselves to continue investing themselves in the subject matter at their own pace. They seem to really care that you want to learn and help you get there. In terms of ease of use and customer service Simplilearn was very strong. It is a matter of simply clicking your course and learning. The support team was great and responded to all my questions via live chat quickly, nicely, and easily. If I had one comment, I would say indicate on your settings when your course runs out. I also had trouble some trouble navigating screams on my surface pro but that was all minimal compared to the benefits. Would definitely recommend.
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Ty Multhaup Itin3D Consultant
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.

Yune Leou-On Market Research and Monetization | Peanut Labs
It was Great!!! My tutors were phenomenal. I took a project overview class and it really helped sharpened my approach on how I world present my final project. The class has been great. I’ve done some self-study on Data Science but then realized that taking it as a course with experts would add some substance to my learning curve.I must admit that my decision to take it with Simplilearn has been the right choice. There is so much detail and hands-on practice in R, SAS and Excel in these classes during the training session. I continue to refresh my reading and benefit from group discussions from SimpliLearn. I’ll absolutely recommend to anyone to give it a try and take one class, and I promise you’ll get more than you expect in content and value."
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Saad Madaha Programmer Analyst III
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.
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Rodney Swann Senior Facility Manager at CBRE
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.
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Saad Madaha Programmer Analyst III - Cardiology Information Systems at New York-Presbyterian Hospital
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.
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Rodney Swann Senior Facility Manager at CBRE
Great experience with the provider, enjoyed learning, very helpful application, and staff support. Good start for mastering R, SAS, and Excel.

Sasa Stevanovic Member of the Network on Institutional Investors and Long-term Investment
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.

Ishanie Niyogi Associate at ICF
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.

Anubhav Ingole Project Management Officer (PMO)/Business Analyst