STAT 1361 - STATISTICAL LEARNING AND DATA SCIENCE

Minimum Credits: 3
Maximum Credits: 3
This course is designed to provide a broad introduction to the field of data science and to expose students to many of the statistical tools most commonly used by modern data scientists. We will explore a wide variety of models and algorithms in a data-driven fashion. Topics will include modeling techniques ranging from classic statistical modeling (e.g. linear and logistic regression) to modern statistical learning (e.g. regularization and lasso)to fundamental machine learning (e.g. random forests and support vector machines). Particular attention will be given to the sorts of scientific questions that can be asked and answered within the different frameworks. Students will have the opportunity to utilize modern, interesting datasets to both provide data-driven analytical solutions and also to formally assess the uncertainty in making such determinations. The R language will be used extensively for statistical computing. Some prior knowledge or experience with R or related programming languages is helpful but not essential
Academic Career: Undergraduate
Course Component: Lecture
Grade Component: LG/SNC Elective Basis
Course Requirements: PREQ: STAT 1261 or STAT 1291 or (STAT 1221 and Knowledge of R)

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