Data Science, Data Analytics, and Decision Modeling with AI: Course Slides

This comprehensive graduate-level course develops students into analytically fluent, AI-powered quantitative business decision-makers. Spanning the full analytics spectrum from probability theory, hypothesis testing, and foundational statistical modeling through advanced concepts with machine learning, generative AI, and intelligent automation, students build theoretical and practical competence in applying data science methods to genuine business problems. The course moves from understanding data structures and relationships, through Exploratory Data Analysis, parametric and nonparametric methods, and the full range of exotic regression techniques (Elastic, Lasso, Logistic, Nonlinear, Partial Least Squares, Poisson, Stepwise), to prediction, AI classification (Cluster, Ensemble, KNN, Neural Network, SVM), time-series forecasting (ARIMAX, GARCH), prescriptive optimization, and causal inference with experimental design. Throughout the course, modern AI tools such as Julius AI and ChatGPT serve as practical accelerators for analysis, visualization, and communication. At the same time, students engage in generative AI architecture and prompting, natural language processing, and agentic AI systems, while continuing to work on analytical software applications (Risk Simulator, BizStats, R-Studio, Power BI). The emphasis at every stage is on analytical judgment—knowing which method to apply, how to evaluate its outputs critically, how to recognize its limitations, and how to translate findings into defensible strategic recommendations for executive audiences. The course culminates in AI ethics, governance, and strategic decision-making, equipping students with both rigorous analytical foundations and a practical AI-powered toolkit to translate raw data into responsible, actionable business intelligence. No prior programming experience required; tool fluency, analytical reasoning, and managerial judgment are the core competencies developed.
ASIN: B0H4S2W47D
VSKU: BVV.B0H4S2W47D.G
Condition: Good
Author/Artist:Mun, Dr. Johnathan
Binding: Paperback
Note: Any images shown are stock photographs and product may differ from what is shown.
Condition Notes: The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.