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.