ABSTRACT
This chapter provides an overview of the field and its connection to data science, articulating the fundamental trade-off in mathematical modeling between model efficiency and model complexity. It provides a brief overview of the commercial and open-source software available for operations research methods. The chapter describes four ways in which operations research connects to data science. It describes the key probability principles on which simulation relies and fundamental techniques for generating random variables. The chapter focuses on the role of simulation techniques in statistical and machine learning. Without high-speed computers to analyze data and solve optimization problems, early operations researchers embraced the craft of trading off model complexity for model efficiency.
