ISOM-231 Automatic for the People: Turn Data Into Insight/W R~python
Introduces a detailed overview of statistical learning for data mining, inference, and prediction in order to tackle modern-day data analysis problems. This course is appropriate for students who wish to learn and apply statistical learning tools to analyze data and gain valuable hands-on experience with R. Statistical learning refers to a vast set of tools for modeling and understanding complex datasets. Exciting topics include: Regression, Logistic Regression, Linear Discriminant Analysis, Cross-Validation, Bootstrap, Linear/Non-Linear Model Selection and Regularization, Support Vector Methodology, and Unsupervised Learning via Principal Components Analysis and Clustering Methods. Students learn how to implement each of the statistical learning methods using the popular statistical software package R via hands-on lab sessions.
Prerequisite
Student has completed all of the following course(s) STATS 240 - Introduction to Statistics
Offered
Fall