R is a statistical programming language that allows you to build probabilistic models, perform data science, and build machine learning algorithms. R has a great package ecosystem that enables developers to conduct data visualization to data analysis.

Why should one take this certification?

This online course covers advanced-level concepts in R programming and demonstrates industry best practices. This is an advanced R course with an intensive focus on machine learning concepts in depth and applying them in the real world with R.  

The course starts off with pre-model-building activities such as univariate and bivariate analysis, outlier detection, and missing value treatment featuring the mice package. Then we take a look at linear, non-linear regression modeling and classification models, and will check out the math behind the working of classification algorithms. 

Who will benefit from taking this certification?

Job seekers looking for employment in various IT companies, PSUs or MNCs. Certification in R Programming will benefit data science professionals, data analyst and students who want to learn R programming.

By the end of the course, you will have a solid knowledge of machine learning and the R language itself. You’ll also solve numerous coding challenges throughout the course.

Companies that hire Vskills R Programmers

IT companies, MNCs, Consultancies hire R Programmers for Data Science related opportunities. Companies employing Linux shell scripting include Capgemini, Larsen & Toubro, TCS, Wipro, Zensar, Accenture, Infosys etc.

R Programming Table of Contents


R Programming Table of Contents


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1. Pre-Model Building Steps

  • Performing Univariate Analysis
  • Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
  • Detecting and Treating Outlier
  • Treating Missing Values with `mice`

2. Regression Modelling-In Depth

  • Interpreting Regression Results and Interactions Terms
  • Performing Residual Analysis and Extracting Extreme Observations With Cook’s Distance
  • Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
  • Validating Model Performance on New Data with k-Fold Cross Validation
  • Building Non-Linear Regressors with Splines and GAMs

3. Classification Models and caret Package-In Depth

  • Understanding the Concept and Building Naive Bayes Classifier
  • Building k-Nearest Neighbors Classifier
  • Building Tree Based Models Using RPart, cTree, and C5.0
  • Building Predictive Models with the caret Package
  • Selecting Important Features with RFE, varImp, and Boruta

4. Core Machine Learning-In Depth

  • Understanding Bagging and Building Random Forest Classifier
  • Implementing Stochastic Gradient Boosting with GBM
  • Regularization with Ridge, Lasso, and Elasticnet
  • Building Classifiers and Regressors with XGBoost

5. Unsupervised Learning

  • Clustering with k-means and Principal Components
  • Determining Optimum Number of Clusters
  • Understanding and Implementing Hierarchical Clustering
  • Clustering with Affinity Propagation
  • Building Recommendation Engines

6. Time Series Analysis and Forecasting

  • Stationarity, De-Trend, and De-Seasonalize
  • Understanding the Significance of Lags, ACF, PACF, and CCF
  • Forecasting with Moving Average and Exponential Smoothing
  • Forecasting with Double Exponential and Holt Winters
  • Forecast with ARIMA Modelling

7. Text Analytics-In Depth

  • Corpus, TDM, TF-IDF, and Word Cloud
  • Cosine Similarity and Latent Semantic Analysis
  • Extracting topics with Latent Dirichlet Allocation
  • Sentiment Scoring with tidytext and Syuzhet
  • Classifying Texts with RTextTools

8. ggplot2

  • Manipulating Legend, AddingText, and Annotation
  • Drawing Multiple Plots with Faceting and Changing Layouts
  • Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
  • ggplot2 Extensions and ggplotly

9. Speeding Up R Code

  • Implement Parallel Computing with doParallel and foreach
  • Write Readable and Fast R Code with Pipes and DPlyR
  • Write Super Fast R Code with Minimal Keystrokes Using Data.Table
  • Interface C++ in R with RCpp

10. Build Packages and Submit to CRAN

  • Build, Document, and Host an R Package on GitHub
  • Performing Important Checks before Submitting to CRAN
  • Submitting an R Package to CRAN
“Exam scheduling to be done through user account” / “Exam once scheduled cannot be cancelled”
Date of Examination
Examination Time
01:00 PM - 02:00 PM
02:30 PM - 03:30 PM
04:00 PM - 05:00 PM
05:30 PM - 06:30 PM
10:00 AM - 11:00 AM
11:30 AM - 12:30 PM

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