Datascience with R

This course deals with online Training of data Science. This program can give you with numerous concepts of R programming for data science. This program R for various science is crafted by our leading instructors in R programming. we have a tendency to conjointly give certification coaching that may cause you to prepared for period of time scenarios.

Course Overview

Description– Data Science with R programming course creates you to associate skilled knowledge analytics victimization with the R programming language. This Data science with R course allows you to require your Data Science skills into a range of firms, serving to analyze knowledge and create additional advised business selections. With dedicated mentoring sessions, this R course incorporates up-to-date information to assist you develop job-ready skills. register in our R coaching and build experience in Data science right now .

Overview– The Data Science with R certification course covers knowledge exploration, knowledge visualization, prophetic analytics, and descriptive analytics techniques with the R language. you'll find out about R packages, a way to import and export knowledge in R, knowledge structures in R, numerous applied math ideas, cluster analysis, and prediction. 

Key Features

  • Introduction to basics of R programing language.
  • Install and configuration of R
  • Learn and apply R for information science solutions.
  • Guidance for certification and Guidance in resume building for R programming.

Who should take this course?– This R programming course advantages professionals who are already into programming and wish to alter within the R atmosphere. People with data or statistics also can pursue a career in R programming. Beginners will follow this course. People who have an interest in obtaining certification also can try this course. Randstad reports that pay hikes within the analytics business area unit 50-percent on top of the IT business. Learning R will assist you begin a career in Data science. 

Pre-requisites– Here are a number of the technical ideas you should know before getting into to learn Data science with R. – 

  • 1.Machine Learning.
  • 2.Modeling
  • 3.Statistics.
  • 4.Programming
  • 5.Databases

Course Syllabus

Intro to Data Science with R Programming

Learning Objectives:

Get an overview of the world of data science. Get acquainted with various analysis and visualization tools used in data science.


  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on: No hands-on

  • Intro to R Programming
  • Installing and Loading Libraries
  • Data Structures in R
  • Control & Loop Statements in R
  • Functions in R
  • Loop Functions in R
  • String Manipulation & Regular Expression in R
  • Working with Data in R
  • Data Visualization in R
  • Case Study


  • Know how to install R, R Studio and other libraries
  • Write R Code to understand and implement R Data Structures 
  • Write R Code to implement loop and  control structures in R
  • Write R Code to read and write data from/to R.
  •  Read data not only from CSV files but also using direct connection to various databases
  • Write R Code to implement ggplot for data visualization
  • Complex Real-Life Data Manipulation, Preparation & Exploratory Data Analysis case study

Probability & Statistics

Learning Objectives:

This module explores basics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range and get basic summaries about data and its measures, together with simple graphics analysis.

Through daily life examples, you will understand the basics of probability, marginal probability and its importance with respect to data science. Learn Baye’s theorem and conditional probability, and alternate and null hypothesis including Type1 error, Type2 error, power of the test, and p-value.


  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Advanced Statistics & Predictive Modeling - I

Learning Objectives:

This module analyses Variance and its practical use, covering strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. You will use Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. Further you will learn to enhance model performance by means of various steps like feature engineering & regularization.

Along the way, you will learn about Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis, including methods to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. You will be able to cement the concepts learnt through real life case studies with Linear Regression and PCA & FA.


  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA


  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.  
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling