Description not available at this time

Physical, chemical, and biological factors affecting the fate and transport of inorganic contaminants (including heavy metals) in soil, water and sediment. Sources, chemistry, pedogenic and geochemical behavior of these contaminants and methods used for their analysis. Risk assessment, and remediation technologies, options, and goals. Prerequisites: CHEM 111 & 112, knowledge of college algebra, basic soil science, and transition metal chemistry, or permission of instructor.

Same as PLNTSOIL 793A; required of doctoral candidates.

Doctoral candidates attend weekly seminar meetings which cover various topics.

Master's candidates present results of their thesis research to faculty and graduate students at a specially scheduled seminar.

Master's candidates attend weekly seminar meetings which will cover various topics.

Selected research problems not related to a candidate's Master's thesis.

Graduate level independent project or research, unrelated to students' thesis or dissertation.

Not available at this time

This course provides an introduction to the statistical techniques that are most applicable to data science. Topics include regression, classification, resampling, linear model selection and regularization, tree-based methods, support vector machines and unsupervised learning. The course includes a computing component using statistical software. Students must have prior experience with a statistical programming language such as R, Python or MATLAB.