Data-Driven Project Management with Python: Optimizing Schedules, Simulating Risk and Analyzing Project Performance through 10 Example Experiments (Management for Professionals)

This book explores how project scheduling, risk analysis and control can be understood, tested and taught through data-driven experimentation.It presents 10 Python-based example experiments that guide readers from fundamental scheduling techniques to advanced project control methods.All project data and code are provided, allowing readers to reproduce, modify, and extend every analysis.The first part introduces the Critical Path Method as the foundation for structured scheduling and extends it to time-cost optimization and resource-constrained scheduling through heuristics and integer programming.The second part employs Monte Carlo simulation to capture schedule uncertainty and to measure activity sensitivity for both unconstrained and resource-limited projects.The third part focuses on project control, using Earned Value Management (EVM) to replicate forecasting accuracy studies from academic literature.The book’s distinctive contribution lies in linking theoretical scheduling princip