Simulation Models for Data Science - by Dan Sullivan (Paperback)

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Book Synopsis Organizations now rely on data and machine learning to guide decisions, yet questions about future actions remain. Historical analysis explains what occurred in the past and predictive models estimate outcomes, but neither explores alternative scenarios. Simulation modeling fills this gap, letting analysts ask 'what if' questions, experiment with change, and study how systems behave under different conditions before decisions are implemented. In Simulation Models for Data Science , Dan Sullivan presents an introduction to four foundational simulation approaches used in data science and operations research: Monte Carlo methods, discrete event simulation, system dynamics, and ag…