This course is intended to provide cross-disciplinary training at the intersection of computational and agricultural sciences. This course will provide students with a broad-based foundational knowledge across disciplines to augment their within-discipline training obtained as part of their graduate program. The course will cover computational agriculture at three levels: first, the biology and biological data collection; second, the computational and data science behind how to analyze agricultural data; and third, a hands-on opportunity to develop state-of-the-art approaches to utilize agricultural data within a team research project. Students will be exposed to on-going projects on campus and will complete the term project in diverse teams. Prerequisite(s): Instructor approval required. Note: Students with credit for PLSC 878 will not receive credit for this course.
This course is intended to provide cross-disciplinary training at the intersection of computational and agricultural sciences. This course will provide students with a broad-based foundational knowledge across disciplines to augment their within-discipline training obtained as part of their graduate program. The course will cover computational agriculture at three levels: first, the biology and biological data collection; second, the computational and data science behind how to analyze agricultural data; and third, a hands-on opportunity to develop state-of-the-art approaches to utilize agricultural data within a team research project. Students will be exposed to on-going projects on campus and will complete the term project in diverse teams. Prerequisite(s): Instructor approval required. Note: Students with credit for PLSC 878 will not receive credit for this course.
