Big Data Analytics
In this course, large data sets will be leveraged to solve challenging analytics problems. With more samples, analytics can use more complex learning models to automate more feature combinations for more robust model tuning, selection, and validation. Parallel, distributed processing will be performed with Apache Spark and Hadoop.
((Database experience: COMP 251 OR COMP 305 OR COMP 353) AND (Analytics experience: COMP 300 OR COMP 379 OR STAT 338 OR STAT 308)) OR permission of instructor.
Outcomes: Python or R will be used with parallel frameworks to perform proper model selection when testing large combinations of features, models, hyperparameters, and ensembles, with additional emphasis on deep learning.
((Database experience: COMP 251 OR COMP 305 OR COMP 353) AND (Analytics experience: COMP 300 OR COMP 379 OR STAT 338 OR STAT 308)) OR permission of instructor.
Outcomes: Python or R will be used with parallel frameworks to perform proper model selection when testing large combinations of features, models, hyperparameters, and ensembles, with additional emphasis on deep learning.