Curated list of the top free MOOCs for learning data analysis using the R programming language.
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Fundamental R programming concepts, data structures, and basic statistical analysis. Free to audit.
10‑course series covering R basics, data cleaning, exploratory analysis, and reproducible research. Audit for free.
Three-part series teaching R for biostatistics, visualization, and reproducibility. Free audit option.
Interactive, hands‑on introduction to R fundamentals. First chapter is free.
Beginner‑level course covering data wrangling, visualization, and basic statistics with R.
Practical guide to data manipulation, visualization, and reporting using tidyverse packages.
Core R programming, data structures, and basic statistical inference.
Focus on data visualization in R using ggplot2 and interactive graphics.
Statistical foundations for data science with R‑based examples.
Classic textbook and video series covering statistical learning methods in R.
Intermediate R topics: functions, debugging, and data pipelines.
Advanced visualization techniques using ggplot2, plotly, and Shiny.
Applied data analysis workflow: import, clean, explore, and model data in R.
Create reproducible R Markdown reports and Shiny apps for data products.
Exploratory data analysis, hypothesis testing, and regression modeling in R.
Focus on data manipulation with dplyr, tidyr, and data visualization.
Beginner-friendly overview of R syntax, data frames, and basic plots.
Practical data analysis workflow using tidyverse and base R.
Comprehensive introduction to data wrangling, visualization, and modeling.
Statistical inference, regression, and classification techniques using R.
Deep dive into statistical modeling and simulation with R.
End‑to‑end data science pipeline: data cleaning, EDA, modeling, and communication.
Core R concepts, data structures, and basic statistical tests.
Advanced visual storytelling with ggplot2, interactive maps, and dashboards.
Free, self‑paced course covering tidyverse, data visualization, and machine learning.
Statistical analysis, hypothesis testing, and reproducible reporting.
Real‑world case studies applying R to business, health, and social data.
Tidyverse workflow, data wrangling, and predictive modeling.
Free introductory module covering R basics, data import, and simple plots.