DSCI 622 - Modeling and Predictive Analytics in Healthcare

(3 credits)

The focus of this course will be on supervised and unsupervised learning methods with applications to healthcare and medical research. The student will be exposed to a variety of machine learning algorithms for uncovering structure in data and predicting outcomes of interest. Unsupervised learning methods covered include clustering and dimension reduction methods. A variety of supervised learning methods for predicting both numeric and non-numeric outcomes will be covered such as tree-based methods and neural networks. Students will gain hands-on experience through course assignments and a final project. Prerequisite: STAT 601 - Statistical Methods for Health Care Research or STAT/HLA 301 (see undergraduate catalog). Grade or P/NC. Offered annually (usually spring semester).

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