R-Programming Training Syllabus:
R is a powerful open-source programming language and software
environment primarily used for statistical computing, data analysis, and
graphical representation. Developed by statisticians, R has become one
of the most widely used tools in data science, offering extensive
libraries for data manipulation, visualization, and machine learning.
Introduction and Setting up Your R-programming Environment
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Lesson 1. Introduction to R
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Lesson 2.
Overview of R: History,use cases,Installing R and RStudio: Setup,
installation, ,Basic R Syntax: Variables, data types,
operators,Writing Your First R Script: Running code, using the
console, and working with R scripts
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Lesson 3.
Data Structures in R:Vectors,Matrices,Lists,Data
Frames,Factors
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Lesson 4.
Data Manipulation:Subsetting and Filtering Data,Basic Data
Operations,Working with Missing Data
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Lesson 5.
Data Visualization:Basic Plotting in R,Introduction to
ggplot2,Customizing Plots,Advanced Visualizations
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Lesson 6.
Functions and Control Structures:Writing Functions in
R,Conditional Statements,Loops,Applying Functions over Data
Structures
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Lesson 7.
Working with Data:Importing Data,Exporting Data,Connecting to
Databases,Handling Dates and Times
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Lesson 8.
Statistical Analysis:Descriptive Statistics,Hypothesis
Testing,Correlation and Covariance
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Lesson 9.
Regression Analysis:Simple Linear Regression,Multiple Linear
Regression,Logistic Regression,Evaluating Model
Performance
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Lesson 10.
Machine Learning in R:Introduction to Machine
Learning,Classification Techniques,Clustering Techniques
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Lesson 11.
R for Data Science:Exploratory Data Analysis (EDA),Data
Cleaning
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Lesson 12.
Class Inheritance,Encapsulation, Polymorphism, and
Abstraction,
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Lesson 13.
Advanced R Topics:R for Time Series Analysis,R for Text Mining,R
for Web Scraping,Parallel Computing in R
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Lesson 14.
Final Project: Apply R skills in a real-world data science
project
