Course Descriptions and Schedule

Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.

MSDS 410-DL : Supervised Learning Methods


Description

This course introduces traditional statistics and data modeling for supervised learning problems, as employed in observational and experimental research. With supervised learning there is a clear distinction between explanatory and response variables. The objective is to predict responses, whether they be quantitative as with multiple regression or categorical as with logistic regression and multinomial logit models. Students work on research and programming assignments, exploring data, identifying appropriate models, and validating models. They utilize techniques for observational and experimental research design, data visualization, variable transformation, model diagnostics, and model selection. 

This is a required course for the Analytics and Modeling specialization. 

Prerequisites: MSDS 400-DL Math for Data Scientists and MSDS 401-DL Applied Statistics with R.



Spring 2021
Start/End DatesDay(s)TimeBuildingSection
03/29/21 - 06/06/21Optional Sync Tu
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Wilck, Joe
Online
Open

Spring 2021
Start/End DatesDay(s)TimeBuildingSection
03/29/21 - 06/06/21Optional Sync Tu
7 – 9:30 p.m. 56
InstructorCourse LocationStatusCAESAR Course ID
Bhatti, Chad
Online
Open

Summer 2021
Start/End DatesDay(s)TimeBuildingSection
06/21/21 - 08/29/21Optional Sync Tu
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Srinivasan, Syamala
Online
Open

Summer 2021
Start/End DatesDay(s)TimeBuildingSection
06/21/21 - 08/29/21Optional Sync M
7 – 9:30 p.m. 56
InstructorCourse LocationStatusCAESAR Course ID
Ott, Melvin
Online
Open
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