Overview
Description
This course provides a comprehensive understanding of data analysis, focusing on statistical learning, model selection, regression techniques, classification methods, and advanced data modeling. Students will gain hands-on experience with advanced statistical methods to solve complex business problems.
Requirements
Prerequisites
- admission to the MS in Information Systems (MSIS)
BADM 700
Original catalog text
Prerequisites
Prerequisite(s): BADM 700 or admission to the MS in Information Systems (MSIS)
Units
Lecture3
Catalog Details
Offering
Offered: Every Spring
Terms
spring
Restrictions
admission to the MS in Information Systems (MSIS)
Attributes
Standard
Learning Outcomes
- apply statistical learning techniques to identify appropriate models for data analysis.
- implement resampling methods such as cross-validation and bootstrap to assess model accuracy.
- perform linear and multiple regression analysis, utilizing variable selection and shrinkage techniques to optimize models.
- apply diagnostics and validation techniques to ensure robust regression models.
- utilize dimension reduction methods to manage high-dimensional data, with an emphasis on applications in regression contexts.
- model non-linear relationships using advanced regression techniques.
- implement basic classification methods and understand their applications in data analytics.