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R Certification Course Overview

The Data Science with R programming certification training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting. Data Science with R offers a range of key features and capabilities that make it a popular choice for data analysis and machine learning tasks

R was originally designed for statistical analysis and provides a comprehensive set of statistical and graphical techniques. It's particularly well-suited for data exploration and hypothesis testing.

Data Science With R Programming Training Key Features

100% Money Back Guarantee
No questions asked refund*
At Fiesttech, we value the trust of our patrons immensely. But, if you feel that this Data Science With R Programming Training does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • Statistical Inference
  • Linear Algebra
  • Advanced Data Visualization
  • Time Series Analysis
  • Machine Learning Algorithms
  • Unsupervised Learning
  • Natural Language Processing (NLP)
  • Data Cleaning and Preprocessing
  • 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
  • Model Interpretability
  • Big Data Integration
  • Optimization Techniques
  • kmeans and DBSCAN clustering

 

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Benefits

The Big Data Analytics market is expected to reach $40.6 billion by 2023, at a growth rate of 29.7-percent. Randstad reports that pay hikes in the analytics industry are 50-percent higher than the IT industry. A career in Data Science with R training can open up a wide range of exciting and lucrative opportunities. Data science skills are in high demand across various industries due to the growing importance of data-driven decision-making

 

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REACH OUT TO US FOR MORE INFORMATION


+91 844 844 0724

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Training Options

Explore all of our training options and pick your suitable ones to enroll and start learning with us! We ensure that you will never regret it!

Self-Paced Learning
549
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 7 hands-on R projects to perfect the skills learned
  • Simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support
Online Bootcamp Training
749
  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners

Training Schedule:-

  22ndJan 2024:Weekday Class

  27th Jan 2024:Weekend Class

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

R Certification Course Curriculum

Eligibility

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

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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

Live Course

Self Paced

  • 2.01 - Course Introduction
    04:34
  • 3.01 - 1.001 Overview
    02:45
  • 3.02 - 1.002 Business Decisions and Analytics
    03:32
  • 3.03 - 1.003 Types of Business Analytics
    01:21
  • 3.04 - 1.004 Applications of Business Analytics
    09:00
  • 3.05 - 1.005 Data Science Overview
    01:12
  • 3.06 - 1.006 Conclusion
    02:32
  • 3.07 - Knowledge Check
    01:21
  • 4.01 - 2.001 Overview
    02:11
  • 4.02 - 2.002 Importance of R
    01:21
  • 4.03 - 2.003 Data Types and Variables in R
    03:32
  • 4.04 - 2.004 Operators in R
    03:32
  • 4.05 - 2.005 Conditional Statements in R
    01:21
  • 4.06 - 2.006 Loops in R
    04:34
  • 4.07 - 2.007 R script
    07:28
  • 4.08 - 2.008 Functions in R
    03:32
  • 4.09 - 2.009 Conclusion
    02:11
  • 4.10 - Knowledge Check
    01:12
  • 5.01 - 3.001 Overview
    00:54
  • 5.02 - 3.002 Identifying Data Structures
    16:11
  • 5.03 - 3.003 Demo Identifying Data Structures
    15:00
  • 5.04 - 3.004 Assigning Values to Data Structures
    05:25
  • 5.05 - 3.005 Data Manipulation
    03:32
  • 5.06 - 3.006 Demo Assigning values and applying functions
    08:08
  • 5.07 - 3.007 Conclusion
    00:54
  • 5.08 - Knowledge Check
    02:11
  • 6.01 - 4.001 Overview
    01:12
  • 6.02 - 4.002 Introduction to Data Visualization
    02:45
  • 6.03 - 4.003 Data Visualization using Graphics in R
    16:11
  • 6.04 - 4.004 ggplot2
    12:00
  • 6.05 - 4.005 File Formats of Graphic Outputs
    01:21
  • 6.06 - 4.006 Conclusion
    00:25
  • 6.07 - Knowledge Check
    02:35
  • 7.01 - 5.001 Overview
    00:54
  • 7.02 - 5.002 Introduction to Hypothesis
    03:32
  • 7.03 - 5.003 Types of Hypothesis
    02:32
  • 7.04 - 5.004 Data Sampling
    04:34
  • 7.05 - 5.005 Confidence and Significance Levels
    01:21
  • 7.06 - 5.006 Conclusion
    01:12
  • 7.07 - Knowledge Check
    00:54
  • 8.01 - 6.001 Overview
    01:12
  • 8.02 - 6.002 Hypothesis Test
    03:32
  • 8.03 - 6.003 Parametric Test
    14:32
  • 8.04 - 6.004 Non-Parametric Test
    08:08
  • 8.05 - 6.005 Hypothesis Tests about Population Means
    8:00
  • 8.06 - 6.006 Hypothesis Tests about Population Variance
    01:12
  • 8.07 - 6.007 Hypothesis Tests about Population Proportions
    01:12
  • 8.08 - 6.008 Conclusion
    00:54
  • 8.09 - Knowledge Check
    01:12
  • 9.01 - 7.001 Overview
    01:21
  • 9.02 - 7.002 Introduction to Regression Analysis
    02:35
  • 9.03 - 7.003 Types of Regression Analysis Models
    01:12
  • 9.04 - 7.004 Linear Regression
    08:08
  • 9.05 - 7.005 Demo Simple Linear Regression
    01:21
  • 9.06 - 7.006 Non-Linear Regression
    03:32
  • 9.07 - 7.007 Demo Regression Analysis with Multiple Variables
    13:21
  • 9.08 - 7.008 Cross Validation
    03:32
  • 9.09 - 7.009 Non-Linear to Linear Models
    05:44
  • 9.10 - 7.010 Principal Component Analysis
    03:32
  • 9.11 - 7.011 Factor Analysis
    02:32
  • 9.12 - 7.012 Conclusion
    01:12
  • 9.13 - Knowledge Check
    00:54
  • 10.01 - 8.001 Overview
    00:25
  • 10.02 - 8.002 Classification and Its Types
    02:32
  • 10.03 - 8.003 Logistic Regression
    03:32
  • 10.04 - 8.004 Support Vector Machines
    04:34
  • 10.05 - 8.005 Demo Support Vector Machines
    11:10
  • 10.06 - 8.006 K-Nearest Neighbours
    02:11
  • 10.07 - 8.007 Naive Bayes Classifier
    02:35
  • 10.08 - 8.008 Demo Naive Bayes Classifier
    09:00
  • 10.09 - 8.009 Decision Tree Classification
    09:21
  • 10.10 - 8.010 Demo Decision Tree Classification
    05:44
  • 10.11 - 8.011 Random Forest Classification
    03:32
  • 10.12 - 8.012 Evaluating Classifier Models
    02:45
  • 10.13 - 8.013 Demo K-Fold Cross Validation
    03:32
  • 10.14 - 8.014 Conclusion
    00:54
  • 10.15 - Knowledge Check
    00:54
  • 11.01 - 9.001 Overview
    01:12
  • 11.02 - 9.002 Introduction to Clustering
    02:35
  • 11.03 - 9.003 Clustering Methods
    07:28
  • 11.04 - 9.004 Demo K-means Clustering
    11:10
  • 11.05 - 9.005 Demo Hierarchical Clustering
    06:07
  • 11.06 - 9.006 Conclusion
    01:12
  • 12.01 - 10.001 Overview
    02:32
  • 12.02 - 10.002 Association Rule
    03:32
  • 12.03 - 10.003 Apriori Algorithm
    09:09
  • 12.04 - 10.004 Demo Apriori Algorithm
    08:08
  • 12.05 - 10.005 Conclusion
    01:21
  • 12.06 - Knowledge Check
    01:21
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Data Science With R Programming Training Exam & Certification

 

No, this R course is not officially accredited.

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

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

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

 You can re-attempt it immediately.

Bootcamp

Why Online Bootcamp

How to use R Programming Language for Data Manipulation

R provides powerful tools for data manipulation, including data cleaning, transformation, and merging

How R Programming helps in Data Visualization

R offers a wide range of packages for creating visualizations, including ggplot2, which is known for its grammar of graphics and flexibility.

Does R supports Machine Learning

R provides extensive support for machine learning tasks. Packages like caret, randomForest, and glmnet offer a range of algorithms for classification, regression, clustering, and dimensionality reduction.

Integration with Other Tools

R can seamlessly integrate with other programming languages and tools. For example, you can use R in conjunction with SQL databases, Apache Hadoop, Apache Spark, or Python. Additionally, R provides APIs to connect with external data sources

FAQS

Data Science With R Programming Training Course FAQs

 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.

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

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

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

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

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