2 credits
Fall 2026 Lecture
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 Outcomes1Explain and apply regression-based methods (e.g., linear, logistic, multinomial, and Poisson regression) for quantitative prediction and qualitative classification.
2Analyze variance using ANOVA to interpret model significance, fit, and practical business implications.
3Develop, 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.
4Use Python or R to implement analytics workflows for data preparation, modeling, and interpretation.
5Explore and apply generative AI tools (e.g., ChatGPT, Gemini) to support learning, analysis, and decision design in analytics contexts.
Restrictions
made by Purdue students.