Business Analytics for Agriculture
Welcome
Business Analytics for Agriculture equips students with the theoretical foundations and practical skills to apply data science in agribusiness. The course integrates principles of data science, statistical methods, and machine learning with agricultural case studies, preparing students to analyze and solve sector-specific challenges. It begins with an overview of data science, highlighting algorithms, big data, AI, and business analytics in agriculture. Students gain hands-on experience in research methods, data preparation, and visualization using both R and Python, applying statistical techniques such as t-tests, ANOVA, and Chi-square. The course further introduces machine learning and deep learning approaches, including regression models, clustering, decision trees, and neural networks, with applications in forecasting, precision farming, and IoT-driven solutions. Practical sessions emphasize descriptive, predictive, and prescriptive analytics through case studies using R, Python, Excel, and SPSS, enabling students to translate data into insights for improved agricultural productivity, profitability, and sustainability.
