- 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)