ECOA 5312 - Advanced Quantitative Methods

Credit Hours: 3
Contact Hours Lecture: 3
Contact Hours Lab: 0

This course equips graduate students with advanced quantitative tools for analyzing and solving complex economic and business problems using Python. Emphasis is placed on applied statistics, econometrics, forecasting, and optimization methods for decision-making under constraints. Students will develop proficiency with Python libraries such as pandas, NumPy, statsmodels, scikit-learn, and SciPy to model real-world problems, conduct simulations, and derive evidence-based insights. Key topics include multivariate regression, time-series analysis, forecasting, machine learning for prediction, and optimization techniques such as linear programming, nonlinear optimization, and constrained decision modeling. By the end of the course, students will be able to integrate quantitative reasoning, economic theory, and advanced computational tools to deliver rigorous, data-driven insights for research, policy, and management.

Prerequisites: N/A
Corequisite: N/A

Equivalencies: N/A

Fees: No
Campus: ALP

TCCNS Equivalent Course: N/A

Course Level: Graduate