ANSC 4040

Data Science Applications in Agriculture

Course Description

Course information provided by the 2025-2026 Catalog.

This course provides an experience in challenge-based learning, focusing on data-driven problems in the agricultural sector. Students work in collaborative teams, engaging with farmers and industry stakeholders to understand real-world challenges and develop innovative solutions. Students will use various digital tools essential for modern data science. Key topics include data governance, privacy, collection methods, storage solutions, processing techniques, and advanced visualization. Students will also learn data modeling and effective data presentation. Project management is a core component, teaching students to manage projects, meet deadlines, and deliver timely results. The course emphasizes teamwork, communication, and collaboration, preparing students for diverse and dynamic environments. This course is ideal for those interested in applying data science to real-world problems and making a tangible impact in agriculture.

Prerequisites A statistical course such one of the following: STSCI 2150: Introductory Statistics for Biology, BTRY3010: Biological Statistics I, ILRST 2100: Introductory Statistics.

Distribution Requirements (DLG-AG)

Last 4 Terms Offered (None)

Learning Outcomes

• Explain the fundamentals of data science and digital agriculture including their interdisciplinary challenges.
• Implement the various data science technologies, precision farming techniques and digital tools used in modern agriculture.
• Conduct data collection, visualization, analysis, and interpretation within the context of digital agriculture.
• Acquire practical skills through hands-on programming activities.
• Explore the applications of machine learning and artificial intelligence in agriculture.
• Recognize the significance of, and apply, data privacy and security techniques in digital agriculture.

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