R Programming for Data Analytics & Statistics
Data Frames, Vectors, Dplyr, Ggplot2, Statistical Modeling & Data Visualizations
Learn R programming for statistical computing, data analysis, and visualization. Master data frames, vectors, dplyr data wrangling, and ggplot2 publication-ready plots.
💡 Interactive Cloud IDE & Assessment
You can practice and run every code example directly in your browser with zero setup in ADV Lab Cloud IDE. Complete all chapters to earn your verified Certificate of Completion.
📚 Complete Course Syllabus & Step-by-Step Topics
Chapter 1: R Syntax, Vectors & Matrices
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of R Syntax, Vectors & Matrices in R Programming for Data Analytics & Statistics.
Chapter 2: Factors & Data Frames
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of Factors & Data Frames in R Programming for Data Analytics & Statistics.
Chapter 3: Data Manipulation with dplyr (filter, select, mutate)
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of Data Manipulation with dplyr (filter, select, mutate) in R Programming for Data Analytics & Statistics.
Chapter 4: Data Visualization with ggplot2
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of Data Visualization with ggplot2 in R Programming for Data Analytics & Statistics.
Chapter 5: Statistical Distributions & Hypothesis Testing
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of Statistical Distributions & Hypothesis Testing in R Programming for Data Analytics & Statistics.
Chapter 6: Linear & Logistic Regression in R
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of Linear & Logistic Regression in R in R Programming for Data Analytics & Statistics.
Chapter 7: Importing & Exporting Datasets (CSV, Excel)
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of Importing & Exporting Datasets (CSV, Excel) in R Programming for Data Analytics & Statistics.
Chapter 8: Building Interactive Dashboards with R Shiny
Learn the fundamental theory, syntax blueprint, memory model, architectural best practices, and real-world implementation of Building Interactive Dashboards with R Shiny in R Programming for Data Analytics & Statistics.
❓ Frequently Asked Questions (FAQ)
Is this R Programming for Data Analytics & Statistics tutorial suitable for freshers?
Yes, our curriculum starts from absolute fundamentals and systematically guides you to advanced industry standards with hands-on projects.
How do I run and test R Programming for Data Analytics & Statistics code?
Use our integrated ADV Lab cloud editor to execute and debug code directly in your browser with zero local software installation.
Will I get a Certificate of Completion?
Yes! Upon completing the course topics and assessment, you receive a free, publicly verifiable certificate.