COURSEWORK

MATHEMATICS

• Real Analysis (MATH 409): proof-based study of the real number system including topics like sequences and series, completeness, continuity, differentiation, Riemann integration, and metric spaces

• Introduction to Topology (MATH 436): metric and topological spaces, separation axioms, continuity, compactness, connectedness, and basic homotopy theory

• Mathematical Probability (MATH 411): probability spaces, discrete and continuous random variables, joint distributions, expectation, law of large numbers, central limit theorem

• Mathematics of Contingent Claims (MATH 425): mathematical theory behind asset price dynamics, binomial pricing models, Black-Scholes analysis, hedging, volatility smile, and implied volatility/binomial trees

• Mathematics of Interest (MATH 325): annuities, varying annuitities, internal rate of return (IRR), bond valuation, yield curves, spot and forward rates, and portfolio immunization

Skills: quantitative analysis, rigorous proof-based reasoning, probabilistic modeling, derivatives and fixed-income pricing

STATISTICS

• Principles of Statistics I & II (STAT 211, 212): probability distributions, sampling, estimation, hypothesis testing, regression, experiment design, model specification, and nonparametric techniques

Skills: statistical inference, data analysis, and model specification

OPTIMIZATION

• Operations Research I (ISEN 320): deterministic optimization models, quantitative modeling, linear programming, sensitivity analysis, network models, optimization models built and solved in AMPL

Skills: optimization modeling and quantitative decision-making

PROGRAMMING

• Python [primary language] (CSCE 110, daily professional use)

  • Data cleaning: standardizing messy, multi-source data into analysis-ready format with regex, pandas, and NumPy

  • Data analysis and linkage: designing tiered matching protocols to link datasets with missing, messy, or inconsistent keys

  • Automation & reporting: web retrieval (Selenium), visualization (Matplotlib), and formatted Excel output (xlsxwriter), all in Jupyter notebooks

• Stata (ECMT 463): regression analysis and econometric estimation

• AMPL (ISEN 320): formulating and solving optimization models

• MATLAB (OCNG 451): scientific data analysis and visualization

• Java & JavaScript (CSCE 111): object-oriented programming, interactive web pages 

ECONOMICS

• Introduction to Econometrics (ECMT 463): applying math and statistics to economic data; econometric models and estimation techniques for testing economic relationships, applied in Stata

• Games and Economic Behavior (ECON 459): game theory, strategic decision-making, Nash equilibrium, sequential and simultaneous repeated games, coordination and collective action, and applying theoretical tools in real-world settings

• Microeconomic Theory (ECON 323): consumer choice, decision making under scarcity and competition, market departures from perfect competition, market structure, and general equilibrium

Skills: conducting analysis in Stata, econometric modeling, estimation and interpretation of economic relationships, strategic market analysis