R Courses
R courses focus on statistical computing and data analysis using the R programming language. You'll learn about data manipulation, visualization, and statistical modeling, along with techniques for exploring and interpreting complex datasets. Key topics may include regression analysis, time series analysis, and using packages like ggplot2 and dplyr for enhanced data handling.
Popular R Courses and Certifications
Logical Operations
Skills you'll gain: Ggplot2, R Programming, Plot (Graphics), Data Visualization, Data Manipulation, Statistical Visualization, Statistical Modeling, Data Analysis, Data Science, Statistical Analysis, Statistical Machine Learning, Machine Learning Methods, Machine Learning, Decision Tree Learning, Machine Learning Algorithms, Regression Analysis, Data Structures, Computer Programming, Computer Programming Tools, Software Development
University of Colorado Boulder
Skills you'll gain: Rmarkdown, Ggplot2, R (Software), Data Visualization, Tidyverse (R Package), R Programming, Data Manipulation, Data Analysis, Software Installation, Programming Principles, Aesthetics
Skills you'll gain: Descriptive Statistics, Data Analysis, R Programming, Statistical Analysis, R (Software), Statistical Methods, Statistics, Data Science
Duke University
Skills you'll gain: Sampling (Statistics), Exploratory Data Analysis, R (Software), Probability & Statistics, Statistical Inference, Probability Distribution, R Programming, Data Analysis, Probability, Statistics, Statistical Methods, Statistical Analysis, Statistical Software, Descriptive Statistics
Skills you'll gain: Version Control, GitHub Copilot, R (Software), Statistical Reporting, Data Transformation, Microsoft Visual Studio, Development Environment, Programming Principles, Descriptive Statistics, Scripting, Statistics, Program Development, Code Reusability
Coursera
Skills you'll gain: Data Transformation, Data Analysis, Data Manipulation, R Programming, Data Cleansing, Data Preprocessing, Scripting
Johns Hopkins University
Skills you'll gain: Rmarkdown, Data Visualization, Statistical Visualization, R (Software), Data Import/Export, Data Visualization Software, R Programming, Tidyverse (R Package), Statistical Reporting, Data Manipulation, Data Processing, Software Installation
Johns Hopkins University
Skills you'll gain: Rmarkdown, Version Control, Data Science, R (Software), Git (Version Control System), GitHub, General Science and Research, Data Analysis, R Programming, Software Installation
Universidad Nacional Autónoma de México
Skills you'll gain: R (Software), R Programming, Statistical Programming, Ggplot2, Plot (Graphics), Data Manipulation, Simulations, Statistical Visualization, Data Processing, Graphing, Data Import/Export, Exploratory Data Analysis, Software Installation
Duke University
Skills you'll gain: Exploratory Data Analysis, R (Software), Ggplot2, Tidyverse (R Package), Data Visualization, Version Control, Data Analysis, Plot (Graphics), Statistics, Data Wrangling, Data Science, R Programming, GitHub, Data Transformation, Statistical Analysis, Git (Version Control System), Statistical Programming
Skills you'll gain: R (Software), R Programming, Arithmetic, Statistical Programming, Development Environment, Programming Principles, Data Import/Export
Skills you'll gain: Rmarkdown, Autoencoders, Shiny (R Package), Deep Learning, Recurrent Neural Networks (RNNs), Transfer Learning, Model Evaluation, R (Software), Artificial Intelligence and Machine Learning (AI/ML), Data Import/Export, Classification Algorithms, Reinforcement Learning, R Programming, Machine Learning Methods, Ggplot2, Data Manipulation, Convolutional Neural Networks, Plotly, Applied Machine Learning, Machine Learning Algorithms
How to choose the right R course
Start with what you want to do with R. If you want to use R for statistical analysis, look for courses focusing on statistical modeling, hypothesis testing, and data visualization techniques. If you're interested in R for machine learning applications, choose courses that cover machine learning algorithms, data preprocessing, and model evaluation in R.
