Data Science is an arena of reconstructing unstructured data to a structured model and confer it into knowledge. There are several tools and programming languages that are used in this process, and R is one such effective and required programming language for this purpose. Data Science R Training is one such training program that will help you to learn R Programming for Data Science from basic to advance level.

This training program will let you learn how to use variables, matrix, functions and other models of R programming required for Data Science. At advance level, you learn to use R programming language in collaboration of Machine learning algorithm for Data Science.


  • This course has no specific prerequisites.
  • Fundamental knowledge in any high-level programming language (preferably R) is ideal but not required.
  • Basic knowledge of statistics would be considered as an added advantage.
  • Basic knowledge of computer hardware and software is ideal but not required.

What will you gain after this course

  • Through this course, you can stay on top of others in the talent race.
  • You will be recognized as a professional Data Scientist.
  • You will be recognized as an R Programmer, as well.
  •  With the help of this course, you will be recognized as a Business intelligence (BI) expert.

Course Outline

Day One: Language Basics

  • Course Introduction
  • About Data Science
    • Data Science Definition
    • Process of Doing Data Science.
  • Introducing R Language
  • Variables and Types
  • Control Structures (Loops / Conditionals)
  • R Scalars, Vectors, and Matrices
    • Defining R Vectors
    • Matricies
  • String and Text Manipulation
    • Character data type
    • File IO
  • Lists
  • Functions
    • Introducing Functions
    • Closures
    • lapply/sapply functions
  • DataFrames
  • Labs for all sections

Day Two: Intermediate R Programming

  • DataFrames and File I/O
  • Reading data from files
  • Data Preparation
  • Built-in Datasets
  • Visualization
    • Graphics Package
    • plot() / barplot() / hist() / boxplot() / scatter plot
    • Heat Map
    • ggplot2 package (qplot(), ggplot())
  • Exploration With Dplyr
  • Labs for all sections

Day Three: Advanced Programming With R

  • Statistical Modeling With R
    • Statistical Functions
    • Dealing With NA
    • Distributions (Binomial, Poisson, Normal)
  • Regression
    • Introducing Linear Regressions
  • Recommendations
  • Text Processing (tm package / Wordclouds)
  • Clustering
    • Introduction to Clustering
    • KMeans
  • Classification
    • Introduction to Classification
    • Naive Bayes
    • Decision Trees
    • Training using caret package
    • Evaluating Algorithms
  • R and Big Data
    • Connecting R to databases
    • Big Data Ecosystem
  • Labs for all sections

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