Statistics for Business
Across foundational and honors undergraduate courses, I teach business statistics focused on developing quantitative reasoning and practical data analysis for managerial decision-making. The curriculum establishes core statistical fundamentals, including descriptive measures, probability distributions, confidence intervals, and hypothesis testing. It then advances to multi-group comparisons, simple and multiple linear regression, time series forecasting, decision analysis, and introductory optimization. Students actively apply these methods through hands-on spreadsheet modeling in Microsoft Excel and business simulation games that mirror operational decision-making under uncertainty.
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QMB 2100, Bus Economic Statistics I
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QMB 3253, Advanced Statistics
Artificial Intelligence and Analytics
This course prepares undergraduate business students to become AI-ready decision-makers across modern organizations. Students learn to translate business problems into data-driven analytical models and automated workflows. The curriculum moves from foundational analytics and visualization in Tableau to applied machine learning, scenario forecasting, and retrieval-augmented generation (RAG). Students then advance to designing agentic workflows with built-in audit trails, human-in-the-loop approvals, and governance guardrails. Coursework culminates in business simulations and an executive capstone proposal evaluating implementation feasibility, risk mitigation, and ROI.
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CAI 3801 – AI and Analytics
ML for Pharmacy Graduate Students
As part of my interdisciplinary outreach, I engage with diverse student cohorts outside the business school to explore how modern data science intersects with other specialized domains. This module examines the core paradigms of machine learning, contrasting statistical inference with predictive algorithms across decision trees, gradient boosting, and deep neural networks. Using real-world case studies spanning computer vision, clinical text analytics, and dynamic survival modeling, students explore how pattern recognition operates across structured data, imaging, audio, and sensor streams. These cross-disciplinary sessions provide a valuable window into how students from varying backgrounds frame technical challenges and what application areas they are prioritizing in their own fields.
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PHA 6261, Healthcare Innovation 3