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Certificate in R Programming

How It Works

  1. 1. Select Certification & Register
  2. 2. Receive a.) Online e Learning Access (LMS)    b.) Hard copy - study material
  3. 3. Take exam online anywhere, anytime
  4. 4. Get certified & Increase Employability

Test Details

  • Duration: 60 minutes
  • No. of questions: 50
  • Maximum marks: 50, Passing marks: 25 (50%).
  • There is NO negative marking in this module.
  • Online exam.

Benefits of Certification


Rs.3,499 /-
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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

https://www.vskills.in/certification/r-programming-table-of-contents

R Programming Table of Contents

https://www.vskills.in/practice/r-programming-mock-test

R Programming Interview Questions

https://www.vskills.in/interview-questions/management-interview-questions/r-programming-interview-questions

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TABLE OF CONTENT

R Language Basics

  • Overview of R Programming Language
  • Installing R
  • R Interactive Command Line
  • R Scripts
  • Hello World
  • R Data types
  • Comments
  • Constants and Variables
  • Operators
  • R Conditional Statements
  • R Functions
  • R Arrays
  • R Strings
  • R Lists
  • Vectors in R
  • R Matrices
  • Factors in R
  • Data Frames in R
  • R Package and Repositories

Introduction to Statistics

  • Terminologies
  • Inferential Statistics
  • Descriptive Statistics
  • Frequency Distribution
  • Cumulative Frequency Distribution
  • Central Limit Theorem
  • Valid Statistical Conclusions
  • Graphical Methods
  • Univariate Analysis
  • Bivariate Analysis
  • Regression

Introduction to Probability

  • Concepts
  • Permutation and Combination
  • Probabilistic Distributions
  • Continuous Variables PDFs
  • Discrete Variables PDFs

Regression Modelling

  • Regression Modeling Basics
  • Correlation and Regression Coefficient
  • Linear Regression
  • Interpreting Regression Results
  • Interaction Effects
  • Residual Analysis
  • Cook’s Distance
  • Hypothesis Testing
  • k-Fold Cross Validation
  • GAM - Generalized Additive Models

Classification Models

  • Naive Bayes Classifier
  • k-Nearest Neighbors Classifier
  • RPart, cTree, and C5.0
  • caret Package
  • Variable selection with RFE, varImp, and Boruta

Machine Learning

  • Random Forest Classifier
  • Gradient Boosting and GBM
  • Regularization - Ridge, Lasso, and Elasticnet
  • XGBoost

Unsupervised Learning

  • Clustering
  • k-means Clustering
  • Principal Components Analysis
  • Determining Optimum Number of Clusters
  • Hierarchical Clustering
  • Affinity Propagation
  • Building Recommendation Engines

Time Series Analysis and Forecasting

  • Stationarity, De-Trend, and De-Seasonalize
  • Lags, ACF, PACF, and CCF
  • Moving Average and Exponential Smoothing
  • Double Exponential and Holt Winters
  • ARIMA Modelling

Text Analytics

  • 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

ggplot2

  • Installation
  • Usage
  • ggplot df
  • The Layers
  • The Labels
  • The Theme
  • The Facets
  • Plot multiple timeseries on same ggplot
  • Bar charts
  • Adjust X and Y axis limits
  • Legend - Deleting and Changing Position
  • Plot margin and background
  • Annotation

Speeding Up R Code

  • doParallel and foreach
  • DPlyR
  • Data.Table
  • RCpp

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