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Data Science

Course Descriptions and Schedule

MSDS 411-DL : Generalized Linear Models


Description

This course extends linear ordinary least-squares regression, introducing the concept of the generalized linear model and its use in making predictions. The course reviews traditional linear regression as a special case of generalized linear models, and then continues with logistic regression, Poisson regression, and survival analysis. The course is heavily weighted towards practical application with large data sets containing missing values and outliers. It addresses issues of data preparation, model development, validation, and deployment.


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

Prerequisites: MSDS 410-DL Regression and Multivariate Analysis.

Starting in spring 2019, all sections will be taught in R.



Winter 2019
Start/End DatesDay(s)TimeBuildingSection
01/07/19 - 03/25/19Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Wilck, Joe
Online
Open
Schedule Notes :

Section-specific language - R

Course Materials :

Section-specific language - R


Winter 2019
Start/End DatesDay(s)TimeBuildingSection
01/07/19 - 03/25/19Optional Sync Th
7 – 9:30 p.m. 56
InstructorCourse LocationStatusCAESAR Course ID
Wilck, Joe
Online
Open
Schedule Notes :

Section-specific language - R

Course Materials :

Section-specific language - R


Winter 2019
Start/End DatesDay(s)TimeBuildingSection
01/07/19 - 03/25/19Optional Sync M
7 – 9:30 p.m. 58
InstructorCourse LocationStatusCAESAR Course ID
Ott, Melvin
Online
Open
Schedule Notes :

Section-specific language - Python

Course Materials :

Section-specific language - Python


Spring 2019
Start/End DatesDay(s)TimeBuildingSection
04/01/19 - 06/09/19Optional Sync M
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Mickelson, William
Online
Open

Spring 2019
Start/End DatesDay(s)TimeBuildingSection
04/01/19 - 06/09/19Optional Sync W
7 – 9:30 p.m. 56
InstructorCourse LocationStatusCAESAR Course ID
Wilck, Joe
Online
Open
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