Predictive Modeling And Introductory Machine Learning

A second course in statistical modeling with a focus on modern methods. The first part of the course focuses on regression methods, including linear and nonlinear regression, and regression trees, with a short introduction to predictive modeling: training vs. validation data sets, over-fitting and model tuning, and methods to measure performance of regression models. The second portion of the course emphasizes classification modeling techniques such as discriminant analysis, classification trees, and neural networks.

Basic Stat Analysis

Introduction to methods of analyzing data from experiments and surveys; the role of statistics in research, statistical concepts and models; probability and distribution functions; estimation; hypothesis testing; regression and correlation; analysis of single and multiple classification models; analysis of categorical data.

Statistical Computing With Sas

This course aims to teach students to use the SAS statistical programming language and to apply this knowledge appropriately in a variety of settings. Student achievement in the course will rely heavily on performing computational tasks, data management, editing data, running basic statistical procedures, and producing reports using SAS.

Dissertation Residency Credit

Residency credit for dissertation research after the qualifying examination. Students may register for this course in the semester of the qualifying examination. A minimum of two semesters are required as well as continuous enrollment (Fall and Spring) until the dissertation is completed and defended.

Dissertation Residency Credit

Residency credit for dissertation research after the qualifying examination. Students may register for this course in the semester of the qualifying examination. A minimum of two semesters are required as well as continuous enrollment (Fall and Spring) until the dissertation is completed and defended.

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