R Programming
Learn R Programming for data analysis, statistical computing, data visualization, data manipulation, and business analytics through practical projects.
Course Description
The R Programming Training Program is designed to help learners develop practical skills in data analysis, statistical computing, and data visualization. R is widely used by data analysts, statisticians, researchers, and business intelligence professionals to analyze complex datasets and generate meaningful insights.
This course covers R fundamentals, data types, variables, functions, data structures, data manipulation, statistical analysis, and visualization techniques. Students will learn to work with popular R libraries such as dplyr, ggplot2, tidyr, and readr.
Learners will also gain hands-on experience in importing datasets, cleaning data, performing exploratory data analysis, creating visualizations, and generating analytical reports.
By the end of the course, students will be able to analyze real-world datasets and develop data-driven solutions using R Programming.
Trainer
N
Navdeep Singh
Data Analytics and Enterprise Data Architecture
20 years experience
Experienced Data Analytics Trainer with expertise in transforming raw data into actionable business insights. Skilled in data visualization, statistical analysis, business intelligence, and reporting....
Technologies Covered
R Programming
RStudio
Base R
dplyr
ggplot2
tidyr
readr
Data Manipulation
Statistical Analysis
Data Visualization
Exploratory Data Analysis
Projects
- Sales Data Analysis Using R
- Customer Segmentation Analysis
- Student Performance Analysis
- Data Visualization Dashboard Using ggplot2
- Exploratory Data Analysis on Real-World Datasets
FAQ
R Programming is used for statistical computing, data analysis, data visualization, predictive analytics, and processing large datasets.
Basic programming knowledge is helpful but not mandatory. Learners can start with R fundamentals and gradually develop their programming and analytical skills.
Learners can explore roles such as R Programmer, Data Analyst, Statistical Analyst, Business Analyst, and Data Science Associate.