Course description

  • Why Learn Data Science with R-Programming Now?

    • Randstad reports that pay hikes in the analytics industry are 50% higher than IT
    • This course forms an ideal package for ambitious data analysts aiming at building a successful career in data science/analytics. By the end of this training, aspirants will gain a 360-degree overview of business analytics and R by mastering concepts like predictive analytics, data visualization, data exploration, etc
    • Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
    • According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019

  • objectives of this online data science training course?

     

    The Data Science certification course in Pune has been designed to provide a comprehensive knowledge of the different data analysis techniques that can be performed using the R language. The data science course in Pune is packed with case studies and real-life projects and makes use of R CloudLab for practice.

    • Mastering R language: The data science course gives a thorough understanding of R packages, the R language, and R-studio. Candidates will get an understanding of the data structure in R; learn the various types of apply functions including DPYR, and perform data visualizations using the various graphics available in R.

    • Mastering advanced statistical concepts: The data science certification course includes several statistical concepts including logistic and linear regression, cluster analysis and forecasting. Candidates will also learn hypothesis testing.

    Candidates have to carry out real-life projects using CloudLab as part of the R language certification course.

    The mandatory projects are distributed over four case studies in the domains of retail, healthcare, and the Internet. R CloudLab has been provided to make sure the candidates can get practical, hands-on experience with their new skills. For further practice, four extra projects are also made available.

  • What skills will you learn in this Data Science Course?

    This data science certification training course in Pune will empower candidates to:

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

  • Who should take this data science certification course in Pune?

     

    A growing demand for proficient data scientists across all enterprises makes this data science certification course well-suited for candidates at all levels of expertise. Simplilearn recommends this Data Science certification training in Pune for the following professionals:

    • Professionals working in data and business analytics
    • IT professionals seeking a career switch into data science and analytics
    • Experienced professionals who would like to utilize data science in their fields
    • Graduates considering to build a career in analytics and data science
    • Software developers considering a profession change to data science and analytics
    • Anyone with a true interest in the data science field

    Prerequisites: This data science online training course does not have any prerequisites. If you are a fresher in the data science field, this course is ideal to begin with.

  • What is CloudLab?

     

    CloudLab is a cloud-based R lab that comes along with this data science course to enable smooth execution of the given project work. With CloudLab, there won’t be a requirement to install and keep R on a virtual machine. Alternately, you will be able to access a preconfigured medium on CloudLab through your browser.

    Candidates will be able to access CloudLab from the Simplilearn Learning Management System (LMS) throughout their course period.

  • What projects are included in this course?

     

    The data science training course in Pune consists of eight real-life, industry-based projects on R CloudLab. As a part of the certification eligibility criteria, successful evaluation of one of the following four projects is necessary.

    Project 1:

    Healthcare: Predictive analytics can be utilized in healthcare to moderate hospital readmissions. In healthcare and other industries, predictors are beneficial when they can be put into action. But real-time and historical data alone are pointless without intervention. More importantly, to judge the efficiency and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend first occurred.

    Project 2:

    Insurance: According to the 2013 Insurance Predictive Modeling Survey, the use of predictive analytics has greatly increased in insurance businesses, especially for big companies. While the survey showed an increase in predictive modeling throughout the industry, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling, compared to 69% of companies with less than that amount of premium.

    Project 3:

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

    Project 4:

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

    Further, four projects have been included to help learners master the R language.

    Project 5:

    Music Industry: Details of the listener’s choices are recorded online. This data is not only used for suggesting 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, anticipate the music choice of the user for targeted advertising.

    Project 6:

    Finance: Candidates will predict whether someone will default or not default on a loan based on user demographic data. Candidates will perform logistic regression by taking into account the characteristics of the borrower and loan features as explanatory variables.

    Project 7:

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

    Project 8:

    Airline: Flight delays are often faced when flying from the Washington DC area to the New York City area. By making use of logistic regression, you can identify flights that are prone to delays. The dataset provided helps with a number of variables including flight times and airports.

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
    • 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
    • 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
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Exam & certification FREE PRACTICE TEST

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

    Online Classroom:

    • Completion of one project
    • Attending one complete batch.

    Online Self-Learning:

    • Completion of one project
    • Completing 85% of the course.

  • Who provides the certification?

     

    Simplilearn will award you the course completion certificate after you successfully complete the Data Science - R Programming certification training.

  • Is this course accredited?

    No, the Data Science - R Programming course in Pune is not officially certified.

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

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

    • Complete any one of the four projects given in this course. You must submit the project deliverables in the LMS, which will be evaluated by Simplilearn’s lead mentor
    • Complete 85% of the course
    • Pass with a minimum score of 80% in the online exam
    • Score at least 60% in any one of the two simulation tests.

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

    You will require around 40 hours to get through the Data Science course successfully.

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

    Candidates will be provided a maximum of three attempts to pass the Data Science - R Programming course exam. Simplilearn offers its learners guidance and support to enable them to clear the exam.

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

     

    Simplilearn provides a lifelong validity for the Data Science - R Programming course certificate.

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

     

    On successfully completing the course and clearing the exam, you will be awarded the certificate through our Learning Management System which can be downloaded or shared through email or Linkedin.

  • Do you offer a money back guarantee?

    Yes. Simplilearn offers a cash-back guarantee for many of its training programs. You can submit refund requests through our Help and Support portal or refer to our Refund Policy.

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

     

    In case you fail the Data Science - R Programming exam, you can retake 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.

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.

Reviews

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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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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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

    • What are the System Requirements?

      Candidates must download RStudio and R from the CRAN website for their operating system. Both R and R Studio are open source software and the installation guidelines will be given in the data science course.

    • Who are our instructors and how are they selected?

       

      Simplilearn’s highly-qualified instructors are all industry experts with decades of relevant teaching experience. All the instructors have undergone a meticulous selection process including a training demo, technical evaluation, and profile screening before getting certified to train for us. It is also assured that instructors with a high alumni rating continue as our faculty.

    • What training formats are used for this course?

       

      Simplilearn follows the training formats mentioned below for the data science with R certification course:

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

      Online Self-Learning: The Online Self-Learning mode provides video lectures that can be viewed at your own pace.

    • What if I miss a class?

       

      We provide the candidates with recordings of each session conducted. Thus, if the candidates miss a class, before attending the next session, they can view the recordings beforehand.

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

      Yes, your enrollment can be canceled if necessary. Your course price will be refunded after deducting the administration fee. Our Refund Policy can be viewed to learn more.

    • Who provides the certification?

       

      After clearing the online exam with a minimum score of 80% and with a satisfactory evaluation of the project by the end of the training, Simplilearn will award your certification stating that you are a certified data scientist with R programming experience.

    • Are there any group discounts for classroom training programs?

       

      Yes, group discounts are offered for our online training programs.Request a Callback or Drop us a Query or use the Live Chat channel to know more about our group discount packages.

    • What payment options are available?

      Candidates can make payment using one of the options given below. The receipt will be sent through email once the payment is done.

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

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

      You can contact Simplilearn by selecting the Live Chat link or the form available on the right side of any page on our website. You can also get more details from our customer service representatives.

    • What is the Expert Assistant Support provided by Simplilearn?

      Expert Assistance includes:

      • Guidance on the forum: Industry experts to respond to candidate’s queries regarding case studies, technical concepts, and projects.
      • Mentoring Sessions: Active Interaction with a subject matter expert to assist candidates with questions in general concerning the project implementation and the course.

      Teaching Assistance includes:

      • Technical Assistance: Questions associated with installation, technical, and administration issues in Data Science with R programming training. During scenarios of critical issues, you will be provided support through a remote desktop.
      • R Programming: Questions associated with the R language while solving and completing projects and case studies
      • Project Assistance: Questions associated with solving and completing case studies and projects, which are part of the Data Science with R programming course

    • How do I contact support?

       

      You can put forth a request to Simplilearn through any of the following channels: Simplitalk, Help & Support, or Live Chat. Within a duration of 48 hours, our teaching assistants will respond to your request.

    • What is Global Teaching Assistance?

      Our instructors are a committed group of subject matter experts who enable the students to get certified in their first attempt. They proactively engage the participants to make sure that the course path is being followed and enhance your learning experience beginning from class onboarding and project mentoring to job assistance. Teaching assistance is available during business hours.

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

       

      Simplilearn offers 24/7 support via chat, email, and calls. Our committed team will provide you with on-demand support via the community forum. Candidates will be given a lifetime access to our community forum, even after they complete the course with us.

    • What is online classroom training?

      Online classroom training for each of the Data Science Certification training class is carried out through live online streaming. The classes are carried out by a certified Data Science instructor with more than 15 years of training and work experience.

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

       

      If candidates register for the online classroom Flexi Pass, they will get access to the pre-recorded videos along with the live training that is conducted online. If candidates register for the self-paced e-learning, they will get access to the pre-recorded videos.

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

      Yes, Simplilearn guarantees success with the course materials and training provided for the Data Science - R Programming certification exam.

    • What certification will I receive after completing the training?

       

      You will receive the course completion certificate from Simplilearn on successfully completing the Data Science - R Programming Certification training.

    • * Disclaimer

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

    Our Pune Correspondence / Mailing address

    Simplilearn Solutions Pvt Ltd, 6th Floor, Pentagon P-2, Magarpatta City, Hadapsar, Pune - 411013, Maharashtra, India, Call us at: 1800-102-9602

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