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Aug 14, 2026
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2026-2027 University Catalog
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QM 67200 - Advanced Analytics And Generative Intelligence
Credit Hours: 2.00. This course integrates classical analytics modeling with emerging generative AI capabilities to support modern, data-driven decision-making. Using business cases and applied projects, students develop skills in quantitative prediction, qualitative classification, regression-based ANOVA, advanced time series forecasting, and analytics programming in Python or R. The course also introduces generative AI tools for learning and decision-making design, with an emphasis on evaluation, ethical reasoning, and judgment-oriented problem-solving across business applications.
Learning Outcomes
1. Explain and apply regression-based methods (e.g., linear, logistic, multinomial, and Poisson regression) for quantitative prediction and qualitative classification.
2. Analyze variance using ANOVA to interpret model significance, fit, and practical business implications.
3. Develop, estimate, and evaluate time series models for forecasting, including smoothing, decomposition, and ARIMA methods, and explore volatility models (e.g., ARCH, GARCH) to analyze time-varying variance.
4. Use Python or R to implement analytics workflows for data preparation, modeling, and interpretation.
5. Explore and apply generative AI tools (e.g., ChatGPT, Gemini) to support learning, analysis, and decision design in analytics contexts.
Credit Hours: 2.00
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