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R Programming Course Syllabus

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In our R Programming Course Syllabus, you’ll grasp the essentials of R programming for statistical computing and graphics. You’ll delve into data manipulation, visualization, statistical analysis, and machine learning with R. The curriculum emphasizes utilizing R for activities such as data cleaning, exploration, and model construction.

Download our R Programming course syllabus PDF for the best R Programming Training Institute in Chennai.

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Breakdown of R Programming Course Fee and Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
April 2024
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
April 2024
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

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Syllabus for The R Programming Course

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Module: 1 R Introduction

1

  • Overview of R Programming
  • Downloading and installing
  • Help of Function
  • Viewing documentation
  • General issues in R
  • Package Management
Module: 2 Data Inputting in R

2

  • Data Types
  • Subsetting
  • Writing data
  • Reading from csv files
  • Creating a vector and vector operation
  • Initializing data frame
  • Control structure
  • Re-directing R Output
Module: 3 Data Visualization

3

  • Creating bar chart and dot plot
  • Creating histogram and box plot
  • Plotting with base graphics
  • Plotting and coloring in R
Module: 4 Basic Statistic

4

  • Computing Basic Statistics
  • Comparing means of two samples
  • Testing a proportion
  • Data Munging Basics
Module: 5 Functions and Programming in R

5

  • Flow control: For loop
  • If condition
  • Debugging tools
Module: 6 Data manipulation in R

6

  • List Management
  • Data Transformation
  • Merging Data Frames
  • Outlier Detection
  • Combining multiple vectors
Module: 7 R an Database

7

  • Performing queries
  • RODBC and DBI Package
  • Advanced Data handling
  • Combined and restructuring data frames
Statistical Modelling in R

8

  • Logical Regression
  • Hierarchical Clustering PCA for Dimensionality Reduction

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