Integration of time series data into analytics and machine learning; visualization methods for temporal data in business and engineering; incorporation of ARIMA modeling techniques into machine learning models; interpretation and validation of time series models for recommending business and engineering recommendations and actions. Prerequisites: Enrollment in Masters of Science in Analytics Credits 1 to 4. 1 to 4 Lecture Hours.
Integration of time series data into analytics and machine learning; visualization methods for temporal data in business and engineering; incorporation of ARIMA modeling techniques into machine learning models; interpretation and validation of time series models for recommending business and engineering recommendations and actions. Prerequisites: Enrollment in Masters of Science in Analytics Credits 1 to 4. 1 to 4 Lecture Hours.
