An introduction to data science theory is provided with some focus on analytics. Topics covered include an introduction to R and other appropriate computational platforms, data types, data manipulation, data frames, data visualization, data reporting, statistical/machine learning, classification, clustering, cross-validation, classification and regression trees, gradient boosting, ridge regression, LASSO, and generalized additive models. Familiarity with some computer package, e.g. SAS, Python or MatLab is required. This course includes a scientific communication component.
An introduction to data science theory is provided with some focus on analytics. Topics covered include an introduction to R and other appropriate computational platforms, data types, data manipulation, data frames, data visualization, data reporting, statistical/machine learning, classification, clustering, cross-validation, classification and regression trees, gradient boosting, ridge regression, LASSO, and generalized additive models. Familiarity with some computer package, e.g. SAS, Python or MatLab is required. This course includes a scientific communication component.
