Problem Centricity

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 ResearchResearch Design and Application for Data and Analysis
Introduction to Mathematical ProgrammingExploring and Analyzing Data
Linear ProgrammingStoring and Retrieving Data
Design and Analysis of AlgorithmsApplied Machine Learning
Algorithmic Methods In Nonlinear ProgrammingData Visualization and Communication
Dynamic Systems and Multivariable Control IExperiments and Causal Inference
Computer Methods and ApplicationsBehind the Data: Humans and Values
Probability and Stochastic Processes IScaling Up! Really Big Data
Stochastic Models In Industrial EngineeringApplied Regression and Time Series Analysis
Nonlinear ProgrammingMachine Learning at Scale
Integer ProgrammingSynthetic 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 storesDetermine when a jet engine needs servicing
Optimally schedule customer service representativesPredict whether a chemical compound will have molecular activity
Reduce the processing time of a cancer screening testDetect whether a specific disease is present in an image of the eye
Create a fair schedule for a sports leaguePredict 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.

2 thoughts on “Problem Centricity

  1. Anthony

    Although 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.

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