The second course in the agricultural data analytics sequence will build on the foundations developed in AREC 261. Students will deepen their proficiency in data analysis, and develop further skills in regression modeling, managing extensive datasets, machine learning techniques, forecasting, and causal evaluation. Through these topics, students will be exposed to the frontiers of agricultural data analysis. Weekly hours: 3 Lecture hours and 2 Practicum/Lab hoursPrerequisite(s): AREC 261.3 Note: Students with credit for BPBE 361 or AREC 361 cannot take this course for credit. Note: Students should take AREC 261.3 in term 1 of their second year, and AREC 262.3 in term 2 of their second year.
The second course in the agricultural data analytics sequence will build on the foundations developed in AREC 261. Students will deepen their proficiency in data analysis, and develop further skills in regression modeling, managing extensive datasets, machine learning techniques, forecasting, and causal evaluation. Through these topics, students will be exposed to the frontiers of agricultural data analysis. Weekly hours: 3 Lecture hours and 2 Practicum/Lab hoursPrerequisite(s): AREC 261.3 Note: Students with credit for BPBE 361 or AREC 361 cannot take this course for credit. Note: Students should take AREC 261.3 in term 1 of their second year, and AREC 262.3 in term 2 of their second year.
