DSI-605 Predictive Analytics 2 - Neural Nets and Regression - with R
In this course, students will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. Four modeling techniques will be used: linear regression, logistic regression, discriminant analysis, and neural networks. The course includes hands-on work with R, a free software environment with capabilities for statistical computing. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
Preview the Online Syllabus
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