Learn how to use the R programming language for data science and machine learning and data visualization!
InstructorJose PortillaThe curriculum covers a wide array of techniques including regression, K-nearest neighbors, decision trees, random forests, and neural networks.
Instruction follows a pattern of introducing machine learning concepts followed by dedicated project walkthroughs and solutions.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
The RStudio interface displays R code for data preprocessing and model fitting alongside a neural network architecture visualization showing input features, hidden layers, and weights.
The RStudio environment displays code for data generation alongside a scatter plot visualizing linear and quadratic regression models.
The RStudio IDE displays the mtcars dataset in the console after running a print command, alongside the project file directory.
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Some instruction utilizes R functions that have been deprecated, such as gather; this signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The machine learning sections can feel brief or rushed compared to the extensive coverage of R fundamentals, with some learners noting a lack of depth in specific algorithm logic.
The curriculum aligns well with the goal of becoming a data scientist using R, covering essential setup and manipulation.