- This course was a combination of traditional lectures and lab sessions. Students enhanced their employability and competitiveness in the job market by developing hands-on modeling and programing experiences.

- Section I. Data management using SQL

- Section II. Data modeling using MATLAB

- Section III. Python for finance: Introduction to Python and machine learning

- Section IV. In class lab sessions to develop data and analytics products


- This course provided students a general overview of the credit markets, the economic functions of banks, and credit analysis. Quantitative analysis, business analytics, and SAS programming skills were also incorporated into this course.

- Section I. Introduction and overview of bank, bank function and regulations

- Section II. Quantitative analysis: interest rates estimation, loan and credit risk analytics

- Section III. Case analysis and interpretation: bank management simulations and competitions.


- This course introduced an overview of the fundamentals of finance and its applications in agricultural economics.

- Section I. Financial statements

- Section II. Cash flow projections for various scenarios

- Section III. Risk management in agriculture

- Section IV. Time values of money and investment decisions

- Section V. Financial analysis


·         Introduction to Agricultural Economics (AGEC 105)

·         Economic Analysis for Agribusiness and Management (AGEC 317)

·         Simulation and Forecasting (AGEC 622)

·         Agribusiness Strategy Analysis (AGEC 440)

·         Agricultural Cooperation (AGEC 413)

·         Food Marketing (AGEC 314)

·         Farm Management and Finance (AGEC 5010)