Data Science Tools for Classical Operations Research

From Mathematical Optimization to Prescriptive Analytics

Author

Troy Altus

Published

May 7, 2026

Welcome

This book explores the powerful intersection of Operations Research and Machine Learning using Python. It bridges traditional optimization techniques with modern data-driven methods to solve complex decision-making problems.

What You’ll Learn

Core optimization techniques: Linear, Integer, Network, and Nonlinear Programming
Handling uncertainty with Stochastic Optimization
Using supervised and unsupervised learning to support OR decisions
Reinforcement Learning and Simulation Optimization
Building end-to-end prescriptive analytics pipelines

Current Release

Part I: Mathematical Foundations

Linear Algebra for OR and ML
Probability and Statistics Foundations
Mathematical Modeling Principles

Part II: Core Optimization Techniques

Introduction to Operations Research and Machine Learning
Linear Programming
Integer Programming
Network Optimization

Advanced Optimization, Machine Learning for OR, and Integration & Applications are in progress and will be added in upcoming releases.

Start with Linear Algebra Foundations.