R Language Programming Certification Course

Course Overview

• Course Duration:4 Months (Including Lab Work, Internship, and real-world assignment).
• Modes of Training: Online Classes/Offline Training (at selected centers).
• Projects:Available – Real-world data science and R programming projects.

R Language Programming Certification Course

• Learn R programming syntax, functions, and data structures from basics to advanced.
• Perform statistical analysis and hypothesis testing using R libraries.
• Clean, manipulate, and transform datasets using Tidyverse packages.
• Create stunning visualizations with ggplot2 and R Shiny dashboards.
• Analyze real-world datasets using regression, classification, and clustering models.
• Prepare for the R Language Programming credential preparation Examand data science roles.

Overview of R Language Programming Certification Course

The R Programming for Data Science & Statistical Computing Course is designed for learners, developers, and professionals aiming to master data analysis and predictive modeling. It covers R language concepts, data manipulation, and hands-on implementation using advanced libraries like Tidyverse and Caret, providing you with the specialized technical expertise needed to advance your career in the modern digital era.

Through hands-on practice, you'll work on real-world datasets, perform data analysis, and build interactive dashboards. By the end of the course, you'll be ready to earn your R Programming credential preparation and apply your skills in data analytics, business intelligence, or academic research.

1. Introduction to R Programming

• Installing R and RStudio.
• Basic syntax and operations.

2. Data Types and Structures

• Variables and Operators.
• Vectors, Matrices, Lists.
• Data Frames and Factors.

3. Data Manipulation

• Using dplyr and tidyr.
• Data Importing/Exporting.
• Cleaning and transforming data.

4. Control Structures and Functions

• Conditional Statements.
• Writing and using functions.

5. Data Visualization

• Plotting with base R.
• Advanced plotting with ggplot2.