¸£Àû±ÆÕ¾

This course provides an in-depth study of building, validating, and predicting using regression models. Topics include multiple linear regression models with both continuous and categorical predictors, model selection techniques, and residual diagnostics. Bayesian regression models are also explored. Categorical data analysis is covered including contingency table analysis and logistic regression models. Students will gain considerable experience working with data. Software is used throughout the course with the expectation of students being able to produce their own analyses.

P: ³§°ÕÌý511, ³§°ÕÌý513, ³§°ÕÌý517, or equivalent

Typically offered in Spring only

An introduction and overview of non-industrial private forestry in the Southeast United States with emphasis on active forest management. Topics include history of human impact on forests, evolution of forest, forestry practices, timber and non timber management objectives, financial aspects of forest land management, and management planning. One required all day field trip.

Typically offered in Fall only

The course will focus on the importance of ethical decision-making in the education and instruction of engineering students. Additionally, it will provide a platform to facilitate the examination and interpretation of complex issues from the perspective of ethical leadership as it relates to the various engineering disciplines.

R: Senior standing or permission of the instructor

GEP Interdisciplinary Perspectives

Typically offered in Fall, Spring, and Summer

As climate and land use changes intensify, they contribute to more frequent and severe climate-driven hazards, making the understanding and application of these tools critical for addressing environmental challenges. Through this course, students will become familiar with the main types of geospatial data (vector, raster, point cloud data). Through hands-on data lectures, students will learn geospatial analytics techniques needed to answer practical questions that interrogate geospatial data to answer questions pertaining to environmental change. Students will become familiar with standard libraries in Python for manipulating vector, raster, and point cloud data and interactions with data APIs.

Prerequisite: ³Ò±õ³§Ìý280 or ³Ò±õ³§Ìý411 or permission of instructor; Restriction: Undergraduates only

Typically offered in Fall only

As climate and land use changes intensify, they contribute to more frequent and severe climate-driven hazards, making the understanding and application of these tools critical for addressing environmental challenges. Through this course, students will become familiar with the main types of geospatial data (vector, raster, point cloud data). Through hands-on data lectures, students will learn geospatial analytics techniques needed to answer practical questions that interrogate geospatial data to answer questions pertaining to environmental change. Students will become familiar with standard libraries in Python for manipulating vector, raster, and point cloud data and interactions with data APIs.

Prerequisite: ³Ò±õ³§Ìý280 or ³Ò±õ³§Ìý411 or permission of instructor; Restriction: Undergraduates only

Typically offered in Fall only