This course introduces students to the fundamental concepts and practical applications of data science within the context of digital agriculture.
Students will learn how to collect, clean, analyze, and visualize agricultural data to support informed decision-making. The course covers data preprocessing, exploratory data analysis, predictive modeling, and basic machine learning techniques applied to real-world agricultural problems such as crop yield prediction, disease detection, soil analysis, irrigation optimization, and resource management.
Hands-on laboratory sessions will provide experience using Python and data science libraries to analyze agricultural datasets. Students will develop practical skills in data interpretation, model evaluation, and data-driven problem solving tailored to smart farming environments.
By the end of the course, students will be able to apply data science techniques to improve agricultural productivity, sustainability, and operational efficiency.