Focus on data science methods that can efficiently and effectively deal with high-dimensional genomic and proteomic data. Topics may include: supervised feature selection, proper methods of model building and validation, discriminant analysis, support vector machines, bagging, random forests and ensemble approach to feature selection and classification.

Prerequisites: DATA 521 or permission of department chair.

4 Credits

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