An introduction to R for data science and prediction methods. Topics include an overview of the R ecosystem, interfaces between R and other programming languages, data handling, programming, model building, data visualization, interactive web apps, the general idea of predictive modeling/algorithms, resampling methods for assessing uncertainty (bootstrap and cross-validation), and elementary methods for regression (e.g., k nearest neighbors) and clustering (k means).
An introduction to R for data science and prediction methods. Topics include an overview of the R ecosystem, interfaces between R and other programming languages, data handling, programming, model building, data visualization, interactive web apps, the general idea of predictive modeling/algorithms, resampling methods for assessing uncertainty (bootstrap and cross-validation), and elementary methods for regression (e.g., k nearest neighbors) and clustering (k means).
