Simulation Models for Data Science: Decoding Complexity

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 agent-based modeling. Combining clear explanations with applied examples and peer-reviewed case studies, this book shows how Python tools and large language models make simulation modeling more accessible. You'll learn how simulation complements statistical modeling and machine learning by revealing bottlenecks, trade-offs, and interactions often hidden in traditional analyses.

• Recognize when to use simulation modeling for complex decisions
• Develop simulation models in Python using four major approaches
• Employ specification-driven development and LLMs to rapidly build models
• Verify and validate models to produce reliable results
• Analyze and present simulation findings to support decision-making
• Deploy and maintain simulation models in production environments