Based on a recent discussion on LinkedIn titled “OR and Data Science”, there appears to be quite a bit of uncertainty surrounding the question of how operations research and data science compare to each other. This uncertainty is surprising since the disciplines of operations research and data science focus on different issues and have different objectives. These differences become evident when you examine the educational backgrounds of operations research analysts and data scientists, the capabilities that employers require them to possess, and the type of projects that they work on.

**Educational Background**

The following table compares the core course list of the University of California Berkeley Master of Information and Data Science program with the North Carolina State University Master of Operations Research program.

**Comparison of Courses**

Operations Research (NC State) | Data Science (UC Berkeley) |
---|---|

Introduction to Operations Research | Research Design and Application for Data and Analysis |

Introduction to Mathematical Programming | Exploring and Analyzing Data |

Linear Programming | Storing and Retrieving Data |

Design and Analysis of Algorithms | Applied Machine Learning |

Algorithmic Methods In Nonlinear Programming | Data Visualization and Communication |

Dynamic Systems and Multivariable Control I | Experiments and Causal Inference |

Computer Methods and Applications | Behind the Data: Humans and Values |

Probability and Stochastic Processes I | Scaling Up! Really Big Data |

Stochastic Models In Industrial Engineering | Applied Regression and Time Series Analysis |

Nonlinear Programming | Machine Learning at Scale |

Integer Programming | Synthetic Capstone Course |

Dynamic Programming | |

Probability and Stochastic Processes II | |

Applied Stochastic Models In Industrial Engineering | |

Queues and Stochastic Service Systems | |

Computer Simulation Techniques | |

Stochastic Simulation Design and Analysis |

It should be noted that there is essentially no overlap between these two lists. Moreover, the operations research program focuses on mathematical modeling of systems and optimization, while the data science program focuses on acquiring, managing and analyzing data and using it for prediction.

**Required Skills**

The job skills for a data scientist and a decision scientist (operations research) that are listed on the COBOT Systems (an analytics startup company) website are shown below:

**Decision Scientist (Operations Research)** – Apply Operations Research & Decision Analytics

Linear Programming (Scheduling, Transportation, Assignment), Dynamic Programming, Integer Programming, Simulation, Queuing, Inventory, Maintenance, Decision Trees/Chains, Markov Chains, Influence Diagrams, Bayesian Networks, Incentive Plans, AHP, MCDM, Game Theory

**Data Scientist** – Apply Statistics & Data Analytics

Clustering, Classification Trees, Correlations, Multiple Regression, Logistic Regression, Forecasting, Sampling & Surveying, Reliability, Data Mining, Design of Experiments, Statistical Quality Control, Statistical Process Control, Machine Learning, Data Visualization

As can be seen, there are no common items on these lists! And, as in the case of the masters programs, the emphasis for operations research is on systems modeling and optimization, while the emphases for data science is on statistical analysis and prediction.

**Type of Projects**

The following table lists operations research projects described in Impact Magazine (British OR Society), and data science projects mentioned by Anthony Goldbloom (founder of Kaggle) in a YouTube video:

**Comparison of Projects**

Operations Research (Impact) | Data Science (Kaggle) |
---|---|

Effectively allocate new product inventory to retail stores | Determine when a jet engine needs servicing |

Optimally schedule customer service representatives | Predict whether a chemical compound will have molecular activity |

Reduce the processing time of a cancer screening test | Detect whether a specific disease is present in an image of the eye |

Create a fair schedule for a sports league | Predict which type of used car will be easiest to sell |

Again, a comparison of these projects tells the same story: operations research projects involve improving or optimizing a system, while data science projects involve analyzing data to make a prediction.

**The Fundamental Difference**

The preceding comparisons highlight the fundamental difference between operations research and data science:

**Operations research** is a problem centric discipline, in which a **mathematical model of a problem** or system is created to improve or optimize that problem or system;

**Data science** is a data centric discipline, in which a **mathematical model of a dataset** is created to discover insights or make a prediction.

kindoflost (@kindoflost)is one predictive and the other prescriptive?

AnthonyAlthough I agree with much of what is said. The projects and skills are not all that different.

Determining when servicing is needed is very similar to scheduling.

Queuing is a stochastic model and differs very little from reliability models. Decision trees are used for the majority of classification type problems.

Also keep in mind that both predictive and prescriptive models are built off of descriptive models.

But you are right. The fundamental difference is the predictive vs prescriptive goals.