Course 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 the Data Science training in Nagpur, 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

Course preview

    • Lesson 00 - Course Introduction

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

      21:06
      • 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

      26:35
      • 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

      50:57
      • 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

      29:40
      • 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

      14:16
      • 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
        04:39
      • 5.006 Conclusion
        01:09
      • Knowledge Check
    • Lesson 06 - Statistics for Data Science-II

      29:55
      • 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

      45:04
      • 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

      1:05:14
      • 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

      28:10
      • 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

      23:13
      • 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
    • Math Refresher

      30:36
      • Math Refresher
        30:36
    • Lesson 1 - Course Objective

      • Learning Objectives
    • Lesson 2 - Defining Data Science

      12:46
      • Learning Objectives
      • 1.1 What is data science
        02:37
      • 1.2 There are many paths to data science
        03:55
      • 1.3 Any advice for new data scientist
        02:59
      • 1.4 What is the cloud
        03:15
    • Lesson 3 - What do Data Science People do

      11:24
      • Learning Objectives
      • 2.1 A day in the life of a data science person
        03:53
      • 2.2 R versus Python
        01:51
      • 2.3 Data science tools and technology
        05:40
    • Lesson 4 - Data Science in Business

      10:40
      • Learning Objectives
      • 3.1 How should companies get started in data science
        03:00
      • 3.2 Recruiting for data science
        07:40
    • Lesson 5 - Use Cases for Data Science

      06:28
      • Learning Objectives
      • 4.1 Applications of data science
        06:28
    • Lesson 6 - Data Science People

      01:05
      • Learning Objectives
      • 5.1 Things data science people say
        01:05
      • Unlocking IBM Certificate
    • Lesson 1 - Welcome

      03:08
      • 1.1 Welcome
        03:08
      • 1.2 Learning Objectives
    • Lesson 2 - R Basics

      13:57
      • 2.1 Learning Objectives
      • 2.2 Math Variables and Strings
        04:25
      • 2.3 Writing Your First R Code
      • 2.4 Vectors and Factors
        04:50
      • 2.5 Vector Operations
        04:42
      • 2.6 Vectors and Factors
    • Lesson 3 - Data Structures in R

      09:29
      • 3.1 Learning Objectives
      • 3.2 Arrays and Matrices
        03:07
      • 3.3 Arrays and Matrices
      • 3.4 Lists
        02:41
      • 3.5 Data Frames
        03:41
      • 3.6 Lists and Dataframes
    • Lesson 4 - R Programming Fundamentals

      17:37
      • 4.1 Learning Objectives
      • 4.2 Conditions and Loops
        04:43
      • 4.3 Conditions and Loops
      • 4.4 Functions in R
        05:55
      • 4.5 Functions in R
      • 4.6 Objects and Classes
        03:25
      • 4.7 Objects and Classes
      • 4.8 Debugging
        03:34
      • 4.9 Debugging
    • Lesson 5 - Working with Data in R

      10:30
      • 5.1 Learning Objectives
      • 5.2 Reading CSV, Excel, and Built-in Datasets
        04:35
      • 5.3 Reading Text (.txt) files in R
        02:40
      • 5.4 Writing and Saving to files in R
        03:15
      • 5.5 Importing Data in R
    • Lesson 6 - Strings and Dates in R

      14:49
      • 6.1 Learning Objectives
      • 6.2 String Operations in R
        04:11
      • 6.3 String Operations
      • 6.4 The Data Format in R
        05:31
      • 6.5 Regular Expressions in R
        05:07
      • 6.6 Regular Expressions
    • Lesson 7 - Course Summary

      03:04
      • Course Summary
        03:04
      • Unlocking IBM Certificate
    • Lesson 1 Introduction

      02:55
      • 1.1 Introduction
        02:55
    • Lesson 2 Sample or population data

      03:56
      • 2.1 Sample or population data
        03:56
    • Lesson 3 The fundamentals of descriptive statistics

      21:18
      • 3.1 The fundamentals of descriptive statistics
        03:18
      • 3.2 Levels of measurement
        02:57
      • 3.3 Categorical variables. Visualization techniques for categorical variables
        04:06
      • 3.4 Numerical variables. Using a frequency distribution table
        03:24
      • 3.5 Histogram charts
        02:27
      • 3.6 Cross tables and scatter plots
        05:06
    • Lesson 4 Measures of central tendency, asymmetry, and variability

      25:17
      • 4.1 Measures of central tendency, asymmetry, and variability
        04:24
      • 4.2 Measuring skewness
        02:43
      • 4.3 Measuring how data is spread out calculating variance
        05:58
      • 4.4 Standard deviation and coefficient of variation
        04:54
      • 4.5 Calculating and understanding covariance
        03:31
      • 4.6 The correlation coefficient
        03:47
    • Lesson 5 Practical example descriptive statistics

      14:30
      • 5.1 Practical example descriptive statistics
        14:30
    • Lesson 6 Distributions

      16:17
      • 6.1 Distributions
        01:02
      • 6.2 What is a distribution
        03:40
      • 6.3 The Normal distribution
        03:45
      • 6.4 The standard normal distribution
        02:51
      • 6.5 Understanding the central limit theorem
        03:40
      • 6.6 Standard error
        01:19
    • Lesson 7 Estimators and Estimates

      23:36
      • 7.1 Estimators and Estimates
        02:36
      • 7.2 Confidence intervals - an invaluable tool for decision making
        06:31
      • 7.3 Calculating confidence intervals within a population with a known variance
        02:30
      • 7.4 Student’s T distribution
        03:14
      • 7.5 Calculating confidence intervals within a population with an unknown variance
        04:07
      • 7.6 What is a margin of error and why is it important in Statistics
        04:38
    • Lesson 8 Confidence intervals advanced topics

      14:27
      • 8.1 Confidence intervals advanced topics
        04:47
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
        04:36
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
        03:40
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
        01:24
    • Lesson 9 Practical example inferential statistics

      09:37
      • 9.1 Practical example inferential statistics
        09:37
    • Lesson 10 Hypothesis testing Introduction

      12:36
      • 10.1 Hypothesis testing Introduction
        04:56
      • 10.2 Establishing a rejection region and a significance level
        04:20
      • 10.3 Type I error vs Type II error
        03:20
    • Lesson 11 Hypothesis testing Let's start testing!

      26:39
      • 11.1 Hypothesis testing Let's start testing!
        06:07
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
        03:55
      • 11.3 Test for the mean. Population variance unknown
        04:26
      • 11.4 Test for the mean. Dependent samples
        04:45
      • 11.5 Test for the mean. Independent samples (Part One)
        03:38
      • 11.6 Test for the mean. Independent samples (Part Two)
        03:48
    • Lesson 12 Practical example hypothesis testing

      06:31
      • 12.1 Practical example hypothesis testing
        06:31
    • Lesson 13 The fundamentals of regression analysis

      18:32
      • 13.1 The fundamentals of regression analysis
        01:02
      • 13.2 Correlation and causation
        04:06
      • 13.3 The linear regression model made easy
        05:02
      • 13.4 What is the difference between correlation and regression
        01:28
      • 13.5 A geometrical representation of the linear regression model
        01:18
      • 13.6 A practical example - Reinforced learning
        05:36
    • Lesson 14 Subtleties of regression analysis

      23:25
      • 14.1 Subtleties of regression analysis
        02:04
      • 14.2 What is Rsquared and how does it help us
        05:00
      • 14.3 The ordinary least squares setting and its practical applications
        02:08
      • 14.4 Studying regression tables
        04:34
      • 14.5 The multiple linear regression model
        02:42
      • 14.6 Adjusted R-squared
        04:57
      • 14.7 What does the F-statistic show us and why we need to understand it
        02:00
    • Lesson 15 Assumptions for linear regression analysis

      19:16
      • 15.1 Assumptions for linear regression analysis
        02:11
      • 15.2 Linearity
        01:40
      • 15.3 No endogeneity
        03:43
      • 15.4 Normality and homoscedasticity
        05:09
      • 15.5 No autocorrelation
        03:11
      • 15.6 No multicollinearity
        03:22
    • Lesson 16 Dealing with categorical data

      05:20
      • 16.1 Dealing with categorical data
        05:20
    • Lesson 17 Practical example regression analysis

      14:42
      • 17.1 Practical example regression analysis
        14:42
    • Lesson 1 Welcome

      02:58
      • Learning Objectives
      • Welcome
        02:58
    • Lesson 2 Basic Visualization Tools

      11:49
      • Learning Objectives
      • Bar Charts
        04:37
      • Histograms
        03:55
      • Pie Charts
        03:17
      • Basic Visualization Tools
    • Lesson 3 Basic Visualization Tools Continued

      09:34
      • Learning Objectives
      • Scatter Plots
        04:46
      • Line Plots and Regression
        04:48
      • Basic Visualization Tools (Continued)
    • Lesson 4 Specialized Visualization Tools

      17:06
      • Learning Objectives
      • Word Clouds
        04:25
      • Radar Charts
        04:08
      • Waffle Charts
        02:57
      • Box Plots
        05:36
      • Word Cloud
      • Radar Charts
      • Waffle Charts
      • Box Plots
    • Lesson 5 How to Create Maps

      04:44
      • Learning Objectives
      • Creating Maps in R
        04:44
      • Maps
    • Lesson 6 How-to-build interactive Webpages

      13:05
      • Learning Objectives
      • Introduction To Shiny
        04:18
      • Creating and Customizing Shiny Apps
        05:28
      • Additional Shiny Features
        03:19
    • Lesson 7 Course Summary

      02:49
      • Course Summary
        02:49
      • Unlocking IBM Certificate
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Exam & certification

  • 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.

  • Do you provide any practice tests as part of this course?

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

Reviews

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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Tanvi Malhotra
Tanvi Malhotra Lead Business Analyst, Bangalore

"It was a great experience to learn Data Science with Simplilearn. It is really convenient to learn at your own pace and time. The material is also good. This course has helped me boost my career. Coming from a Consulting firm, it was mandatory to learn data analytics skills. I really feel confident and I truly appreciate Simplilearn for it. Simplilearn is a Master. The reminders and guidance that I received from them was just beyond expectations! Whenever I had any query, it was resolved! Right on time... THANKS SO MUCH!"

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Chirag Bhatia
Chirag Bhatia Graduate Student at University of Texas at Dallas, Bangalore

I enrolled for Data Science with R, SAS and Excel program at Simplilearn after researching a lot of similar websites who offered similar course. The uniqueness of Simplilearn is their course depth combined with good pricing and friendly customer service. I mean this is what one expects when searching for a good online course. My advice: Take their courses. It will give you a good heads-up to your career.

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Aastha Hissaria
Aastha Hissaria Business Analyst at Manthan Software Services, Bangalore

I could recommend Simplilearn to anyone without hesitation. The courses have great depth and they allow you do work on them at your own pace. The course content is regularly updated and the content is presented in a crisp manner. Simplilearn’s course has added weight to my CV. Customer service support provided by them is top-notch. I would like to especially mention Sheena, as she helped me with the various queries I had. Highly recommend people reading this to go through all the courses they have, pick the course that suits you best, and go ahead with Simplilearn.

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Arul Venkat
Arul Venkat Statistician, Bangalore

It was a very useful and good learning experience. I have learnt the basic statistical concepts and real-time data analysis and interpretation. Got proper knowledge of R and SAS tools. Thanks to the entire Simplilearn Team.

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FAQs

  • 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 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.

  • 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.

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.

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.

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