The principles and practices of open data science are introduced, with emphasis on reproducibility, tidy coding, and open source science workflows. Techniques for managing, visualizing, and analyzing agricultural data are explored through examples relevant to crop and soil sciences. Statistical modeling approaches commonly used in agronomic research are presented, alongside tools for effective data organization and communication. Students are expected to already have some computing literacy.
The principles and practices of open data science are introduced, with emphasis on reproducibility, tidy coding, and open source science workflows. Techniques for managing, visualizing, and analyzing agricultural data are explored through examples relevant to crop and soil sciences. Statistical modeling approaches commonly used in agronomic research are presented, alongside tools for effective data organization and communication. Students are expected to already have some computing literacy.
