Data Science Course Overview

The Data Science with R programming course 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.

Data Science Course Key Features

  • 64 hours of blended 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
  • K-means and DBSCAN clustering

Training Options

Self-Paced Learning

$ 599

  • Lifetime access to high-quality self-paced e-learning content curated by industry experts
  • 24x7 learner assistance and support
Enroll now

Blended Learning

$ 699

  • 90 days of flexible access to online classes
  • Lifetime access to high-quality self-paced e-learning content and live class recordings
  • 24x7 learner assistance and support
  • Classes starting in Fresno from:-
24th Jan: Weekday Class
31st Jan: Weekday Class
Enroll now

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

Data Science Course Curriculum

Eligibility

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

  • Data Science with R

    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

      14:10
      • 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

      30:36Preview
      • Math Refresher
        30:36
  • Free Course

  • Data Science in Real life

    Preview

    • Lesson 1 - Course Objective

      • Learning Objectives

    • Lesson 2 - Defining Data Science

      12:46Preview
      • 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

      06:28Preview
      • 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
  • Free Course

  • R Programming for Data Science

    Preview

    • Lesson 1 - Welcome

      03:08Preview
      • 1.1 Welcome
        03:08
      • 1.2 Learning Objectives

    • Lesson 2 - R Basics

      13:57Preview
      • 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

      17:37Preview
      • 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

      14:49Preview
      • 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
  • Free Course

  • Statistics Essential for Data Science

    Preview

    • Lesson 1 Introduction

      02:55Preview
      • 1.1 Introduction
        02:55

    • Lesson 2 Sample or population data

      03:56Preview
      • 2.1 Sample or population data
        03:56

    • Lesson 3 The fundamentals of descriptive statistics

      21:18Preview
      • 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

      25:17Preview
      • 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

      23:36Preview
      • 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

      09:37Preview
      • 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!

      26:39Preview
      • 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

      06:31Preview
      • 12.1 Practical example hypothesis testing
        06:31

    • Lesson 13 The fundamentals of regression analysis

      18:32Preview
      • 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

      19:16Preview
      • 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
  • Free Course

  • Data Visualization with R

    Preview

    • Lesson 1 Welcome

      02:58Preview
      • Learning Objectives
      • Welcome
        02:58

    • Lesson 2 Basic Visualization Tools

      11:49Preview
      • 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

      17:06Preview
      • 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

      13:05Preview
      • 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

Contact Us

1-844-532-7688

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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 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.

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Data Science Exam & Certification

Data Science Course - R Programming in Fresno, CA

  • 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.

Data Science Course Reviews

  • Savish Dan

    Savish Dan

    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.

  • Ishanie Niyogi

    Ishanie Niyogi

    Associate at ICF

    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.

  • Yune Leou-On

    Yune Leou-On

    Market Research and Monetization | Peanut Labs

    Simplilearn has been a great help for me in my professional and academic progress. I have enjoyed taking their courses and can recommend them to anyone. Currently I have taken their Data Science course and I will now be looking into taking their CAPM program. Their tutors are also top-notch.

  • Rodney Swann

    Rodney Swann

    Senior Facility Manager at CBRE

    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

    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

    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

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

  • Amani Alawneh

    Amani Alawneh

    Head of Project Management

    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

    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.

  • Farhan Nizar

    Farhan Nizar

    Lead Technical Analyst at NBAD

    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.

  • Anubhav Ingole

    Anubhav Ingole

    Project Management Officer (PMO)/Business Analyst

    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

    My trainer was very engaging and knowledgeable. I liked her way of teaching - sharing notes and providing hands-on practicals within the 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

    Simplilearn has designed the python 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 a most logical way. Thanks, Simplilearn

  • Rohit Kumar

    Rohit Kumar

    Consultant

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

  • Lavanya Krishnan

    Lavanya Krishnan

    RePM consultant

    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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Why Simplilearn

Simplilearn’s Blended Learning model brings classroom learningexperience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience

  • Self-Paced Online Video

    A 360-degree learning approach that you can adapt to your learning style

  • Live Virtual Classroom

    Engage and learn more with these live and highly-interactive classes alongside your peers

  • 24/7 Teaching Assistance

    Keep engaged with integrated teaching assistance in your desktop and mobile learning

  • Online Practice Labs

    Projects provide you with sample work to show prospective employers

  • Applied Projects

    Real-world projects relevant to what you’re learning throughout the program

  • Learner Social Forums

    A support team focused on helping you succeed alongside a peer community

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Data Science Training FAQs

  • 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 Fresno, 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 is R?

    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.
     

  • How do beginners learn data science with R?

    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 course on Data Science with R, along with supporting blogs and YouTube videos to help you understand the basics and importance of R in the dynamic field of data science.
     

  • Can I learn data science with R online?

    Learning methodologies have evolved tremendously with the influx of new technology. These changes have increased the ease and efficiency of learning on your terms. Simplilearn's Data Science with R Certification training provides live classes and round-the-clock access to study materials with the help of our learning management system. Our extensive collection of blogs, tutorials, and YouTube videos will help you brush up on the concepts. Even when your class ends, we provide a 24/7 support system to help you with any questions, concerns, or difficulties you may have.

  • What are the System Requirements?

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

  • Who are our instructors and how are they selected?

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

  • What training formats are used for this course?

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

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

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

  • What if I miss a class?

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

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

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

  • Who provides the certification?

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

  • Are there any group discounts for classroom training programs?

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

  • What payment options are available?

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

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

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

  • What is the Expert Assistant Support provided by Simplilearn?

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

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

  • How do I contact support?

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

  • What is Global Teaching Assistance?

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

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

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

  • What is online classroom training?

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

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

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

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

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

  • What certification will I receive after completing the training?

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

  • * Disclaimer

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

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