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Programming & Analytics

R Programming for Statistics

Develop proficiency in R programming language for statistical computing, data visualization, and reproducible research workflows.

€899
Complete Course
10 Weeks
Duration
Hands-on
Approach
View All Courses
RStudio 
# Advanced Statistical Analysis
library(ggplot2)
library(dplyr)

data <- read.csv("dataset.csv")

# Create visualization
ggplot(data, aes(x = variable1, y = variable2)) +
  geom_point(color = "steelblue") +
  geom_smooth(method = "lm") +
  theme_minimal()

# Statistical modeling
model <- lm(outcome ~ predictor1 + predictor2,
           data = data)
summary(model)

Master R Programming

From basic syntax to advanced statistical modeling, become proficient in the world's leading statistical computing language.

R Fundamentals

Master R syntax, data structures, and programming concepts

Data Visualization

Create stunning plots with ggplot2 and advanced graphics

Statistical Modeling

Build predictive models and perform hypothesis testing

Reproducible Research

Create dynamic reports with R Markdown and knitr

Skills You'll Develop

Build a comprehensive R programming skillset for statistical analysis

Data Manipulation with dplyr

Advanced

Filter, select, mutate, and summarize data efficiently

Statistical Analysis

Expert

Perform complex statistical tests and modeling

Data Visualization

Advanced

Create publication-ready charts and graphs

R Markdown Reporting

Intermediate

Generate dynamic reports and presentations

Package Development

Advanced

Build and share custom R packages

Shiny Applications

Intermediate

Create interactive web applications

Course Curriculum

Structured learning path from R basics to advanced applications

Module 1: R Fundamentals

2 weeks

  • • R installation and RStudio setup
  • • Data types and structures
  • • Basic operations and functions
  • • Control structures and loops

Module 2: Data Import & Cleaning

2 weeks

  • • Reading various file formats
  • • Data cleaning techniques
  • • Handling missing values
  • • Data type conversions

Module 3: Data Visualization

2 weeks

  • • ggplot2 fundamentals
  • • Creating various plot types
  • • Customizing themes and aesthetics
  • • Interactive visualizations

Module 4: Statistical Analysis

2 weeks

  • • Descriptive statistics
  • • Hypothesis testing
  • • Correlation and regression
  • • ANOVA and chi-square tests

Module 5: Advanced Topics

1 week

  • • R Markdown and reporting
  • • Package development basics
  • • Shiny applications intro
  • • Version control with Git

Module 6: Capstone Project

1 week

  • • End-to-end data analysis
  • • Professional reporting
  • • Code documentation
  • • Portfolio development

Try R Programming

Experience the power of R with our interactive examples

Data Analysis Example

# Load data and create summary
data <- mtcars
summary(data$mpg)
Min. 1st Qu. Median Mean 3rd Qu. Max.
10.40 15.43 19.20 20.09 22.80 33.90

This simple example shows how R can quickly provide statistical summaries of your data, making it easy to understand patterns and distributions.

Visualization Example

# Create scatter plot
ggplot(mtcars, aes(wt, mpg)) +
geom_point() +
geom_smooth(method = "lm") +
theme_minimal()

With just a few lines of code, R's ggplot2 package creates professional-quality visualizations that reveal relationships in your data.

Start Your R Programming Journey

Join thousands of professionals who have mastered R for statistical analysis

€899
Complete 10-week R Programming Course
6 comprehensive modules
Hands-on projects
Expert instruction
Certificate of completion

30-day money-back guarantee • Flexible payment options available

R Programming Excellence for Statistical Computing

R programming represents the gold standard for statistical computing and data analysis across academic and professional environments. Our comprehensive training program addresses the growing demand for R proficiency by providing structured, hands-on education that transforms beginners into competent statistical programmers.

The course curriculum emphasizes practical application of R programming concepts through real-world datasets and industry-relevant projects. Students develop expertise in data manipulation, visualization, statistical modeling, and reproducible research workflows that align with contemporary best practices in statistical analysis.

Through intensive instruction in R's extensive ecosystem including ggplot2, dplyr, and Shiny, participants acquire the technical skills necessary for advanced statistical roles. The program combines theoretical understanding with practical implementation, ensuring graduates can effectively leverage R's capabilities for complex analytical challenges in their professional careers.