R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

R is a language used for statistical computations, data analysis and graphical representation of data.

Why should one take Data Analysis with R Certification?

The certification is suitable for web developers, programmers and graduates wanting to excel in web application development areas. It is also well suited for those who are already working and would like to take certification for further career progression in web application development.

Earning Vskills Data Analysis with R Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.

Who will benefit from taking Data Analysis with R Certification?

Job seekers looking to find employment in IT or software development departments of various companies, students who want to learn angular 6.

Companies that hire Vskills Data Analysis with R Professional

Job seekers looking to find employment in various companies it companies, students generally wanting to improve their skill set and make their CV stronger and existing employees looking for a better role can prove their employers the value of their skills through this certification.

Apply for Data Analysis with R Certification

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Importing Data in Table Format

  • Importing Data from Tables (read.table)
  • Downloading Open Data from FTP Sites
  • Fixed-Width Format
  • Importing with read.lines (The Last Resort)
  • Cleaning Your Data

Handling the Temporal Component

  • Loading the Required Packages
  • Importing Vector Data (ESRI shp and GeoJSON)
  • Transforming from data.frame to SpatialPointsDataFrame
  • Understanding Projections
  • Basic time/dates formats

Importing Raster Data

  • Introducing the Raster Format
  • Reading Raster Data
  • Mosaicking
  • Stacking to Include the Temporal Component

Exporting Data

  • Exporting Data in Tables
  • Exporting Vector Data (ESRI shp File)
  • Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)
  • Exporting Data for WebGIS Systems (GeoJSON, KML)

Descriptive Statistics

  • Preparing the Dataset
  • Measuring Spread (Standard Deviation and Standard Distance)
  • Understanding Your Data with Plots
  • Plotting for Multivariate Data
  • Finding Outliers

Manipulating Vector Data

  • Introduction
  • Re-Projecting Your Data
  • Intersection
  • Buffer and Distance
  • Union and Overlay

Manipulating Raster Data

  • Introduction
  • Converting Vector/Table Data into Raster
  • Subsetting and Selection
  • Filtering
  • Raster Calculator

Visualizing Spatial Data

  • Plotting Basics
  • Adding Layers
  • Color Scale
  • Creating Multivariate Plots
  • Handling the Temporal Component

Interactive Maps

  • Introduction
  • Plotting Vector Data on Google Maps
  • Adding Layers
  • Plotting Raster Data on Google Maps
  • Using Leaflet to Plot on Open Street Maps

Creating Global Economic Maps with Open Data

  • Introduction
  • Importing Data from the World Bank
  • Adding Geocoding Information
  • Concluding Remarks

Point Pattern Analysis of Crime in the UK

  • Theoretical Background
  • Introduction
  • Intensity and Density
  • Spatial Distribution
  • Modelling

Cluster Analysis of Earthquake Data

  • Theoretical Background
  • Data Preparation
  • K-Means Clustering
  • Optimal Number of Clusters
  • Hierarchical Clustering
  • Concluding

Time Series Analysis of Wind Speed Data

  • Theoretical Background
  • Reading Time-Series in R
  • Subsetting and Temporal Functions
  • Decomposition and Correlation
  • Forecasting


  • Theoretical Background
  • Data Preparation
  • Mapping with Deterministic Estimators
  • Analyzing Trend and Checking Normality
  • Variogram Analysis
  • Mapping with kriging

Regression and Statistical Learning

  • Theoretical Background
  • Dataset
  • Linear Regression
  • Regression Trees
  • Support Vector Machines

“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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