operations research and statistical analysis

What does an operations research analyst do?

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What is an Operations Research Analyst?

An operations research analyst applies advanced analytical and mathematical techniques to solve complex problems and optimize decision-making in various industries. These analysts use mathematical modeling, statistical analysis, and computer simulations to analyze and improve organizational processes, systems, and resource allocation. They work with large sets of data and develop mathematical models and algorithms to assist in decision-making, improve efficiency, and maximize outcomes.

Operations research analysts work on a wide range of problems, including supply chain optimization, production planning, scheduling, inventory management, logistics, and facility layout. They use their expertise to formulate and solve mathematical models that represent real-world scenarios, considering factors such as constraints, uncertainties, and objectives. By analyzing data and running simulations, they can evaluate different scenarios and recommend the best course of action to optimize performance, reduce costs, increase productivity, and improve overall operational efficiency.

What does an Operations Research Analyst do?

Operations research applies quantitative methods and analytical techniques to improve processes, systems, and resource allocation in various industries.

Duties and Responsibilities The duties and responsibilities of an operations research analyst can vary depending on the specific industry, organization, and project requirements. However, here are some common responsibilities associated with this role:

• Problem Identification and Formulation: Operations research analysts work closely with stakeholders to understand the objectives and challenges of a given problem or decision-making process. They identify the key variables, constraints, and objectives and translate them into a mathematical or analytical model.
• Data Collection and Analysis: Analysts gather relevant data from various sources, including databases, surveys, and other sources. They clean and preprocess the data, perform statistical analysis, and apply mathematical modeling techniques to derive insights and patterns.
• Mathematical Modeling and Optimization: Operations research analysts develop mathematical models, algorithms, and optimization techniques to represent the problem at hand. They use tools such as linear programming, integer programming, simulation, and other techniques to analyze the model and identify optimal solutions or decision-making strategies.
• Simulation and Scenario Analysis: Analysts utilize simulation tools and techniques to model complex systems and evaluate different scenarios. They run simulations to assess the impact of various decisions, policies, or system changes on performance metrics and outcomes.
• Decision Support and Recommendations: Based on the analysis and optimization results, operations research analysts provide decision support to stakeholders. They interpret the findings, present recommendations, and communicate the implications of different options to assist in informed decision-making.
• Implementation and Monitoring: Analysts collaborate with relevant teams to implement recommended solutions or changes. They may assist in the deployment of new systems, processes, or strategies and monitor their effectiveness to ensure that the desired outcomes are achieved.
• Continuous Improvement and Research: Operations research analysts stay updated with advancements in the field, continuously explore new techniques and methodologies, and contribute to research and development efforts. They seek opportunities for process improvement and provide ongoing support to optimize operations and decision-making.
• Collaboration and Communication: Analysts work collaboratively with cross-functional teams, stakeholders, and subject matter experts. They communicate complex analytical concepts and findings in a clear and concise manner, both verbally and through reports or presentations.

Fields of Work While operations research analysts can be employed in a wide range of industries, their expertise is particularly valuable in sectors that involve complex operational and logistical challenges. Some common fields where operations research analysts are employed include:

• Transportation and Logistics: Operations research analysts play a vital role in optimizing transportation networks, improving route planning, scheduling, and resource allocation for shipping, distribution, and supply chain management.
• Manufacturing and Production: Operations research analysts work on optimizing production planning, inventory management, scheduling, and facility layout to enhance efficiency, reduce costs, and improve productivity in manufacturing and production processes.
• Healthcare: In the healthcare industry, operations research analysts analyze patient flow, resource allocation, hospital scheduling, healthcare delivery optimization, and healthcare resource planning to improve operational efficiency and patient outcomes.
• Finance and Risk Management: Operations research analysts apply mathematical models and optimization techniques to analyze financial markets, portfolio management, risk assessment, and risk management to help financial institutions make informed decisions and mitigate risks.
• Energy and Utilities: Operations research analysts contribute to optimizing energy production and distribution systems, grid management, resource allocation, and demand forecasting to improve energy efficiency and ensure reliable supply.
• Defense and Homeland Security: Operations research analysts work on strategic planning, resource allocation, logistics, and decision support systems to optimize military operations, defense planning, and homeland security initiatives.
• Consulting and Analytics: Many operations research analysts work in consulting firms or analytics companies, where they provide expertise in optimization, decision support, and data analysis to clients across multiple industries.

Types of Operations Research Analysts Operations research analysts can specialize in different areas based on their expertise and interests. Here are some common types of operations research analysts:

• Supply Chain Analyst: Supply chain analysts focus on optimizing supply chain operations, including demand forecasting, inventory management, distribution network design, transportation optimization, and supplier management. They work on improving efficiency, reducing costs, and enhancing overall supply chain performance.
• Production Planning Analyst: Production planning analysts specialize in optimizing production processes, capacity planning, scheduling, and resource allocation. They develop mathematical models and algorithms to determine the optimal production plan, considering factors such as machine capacity, labor availability, material constraints, and customer demand.
• Pricing Analyst: Pricing analysts focus on developing pricing strategies and models to maximize revenue and profitability. They use mathematical optimization and statistical analysis techniques to analyze market demand, competitor pricing, cost structures, and customer behavior, helping organizations set optimal prices for products and services.
• Financial Analyst : Financial analysts apply operations research techniques to financial planning, risk management, portfolio optimization, and investment decision-making. They develop models and algorithms to analyze financial data, evaluate investment options, and optimize financial performance while considering risk factors.
• Healthcare Analyst: Healthcare analysts apply operations research methods to optimize healthcare delivery systems, resource allocation, patient flow, and healthcare quality. They develop models and algorithms to improve hospital operations, appointment scheduling, staffing, and resource utilization in order to enhance patient outcomes and efficiency.
• Risk Analyst: Risk analysts specialize in assessing and managing risks in various industries. They develop mathematical models and simulation techniques to evaluate and mitigate risks associated with supply chain disruptions, financial investments, project management, and other operational areas.
• Decision Support Analyst: Decision support analysts assist organizations in making informed decisions by providing analytical insights and recommendations. They develop decision support systems, models, and visualization tools that help stakeholders understand complex data, evaluate options, and select the best course of action.
• Optimization Analyst: Optimization analysts focus on solving complex optimization problems using mathematical programming techniques. They develop and implement optimization models to address problems such as resource allocation, workforce scheduling, facility location, and network optimization.

Are you suited to be an operations research analyst?

Operations research analysts have distinct personalities . They tend to be investigative individuals, which means they’re intellectual, introspective, and inquisitive. They are curious, methodical, rational, analytical, and logical. Some of them are also conventional, meaning they’re conscientious and conservative.

Does this sound like you? Take our free career test to find out if operations research analyst is one of your top career matches.

What is the workplace of an Operations Research Analyst like?

Operations research analysts typically work in office settings, whether it's within a company or a consulting firm. They may also work remotely or engage in a combination of on-site and remote work, especially in situations where data and analysis can be accessed electronically. Their work involves extensive use of computers and specialized software tools for mathematical modeling, data analysis, and simulation.

Collaboration is an essential aspect of the work environment for operations research analysts. They often work closely with cross-functional teams, including managers, engineers, data scientists, and subject matter experts. This collaboration is important to gather relevant data, understand business processes, and gain insights into the problem or decision-making context. Operations research analysts may participate in meetings, workshops, or project teams to discuss findings, share progress, and align on goals.

The nature of their work also involves data-intensive tasks. Operations research analysts spend a significant amount of time collecting, cleaning, and analyzing data to inform their models and simulations. They use statistical software, programming languages, and database tools to process and manipulate large datasets. Additionally, they apply mathematical modeling techniques and optimization algorithms to derive insights, explore different scenarios, and identify optimal solutions.

In terms of work schedule, operations research analysts typically work full-time, following regular business hours. However, project deadlines or urgent issues may require flexibility and occasional overtime to meet deliverables. The workload can vary depending on the complexity and scope of the projects they are involved in.

Operations Research Analysts are also known as: OR Analyst Operations Analyst

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Department of Statistics and Operations Research

Introduction

The major in statistics and analytics (STAN) is an excellent program for students interested in statistical data science, operations research, and actuarial science, as well as in fields such as business, economics, public policy and health, psychology, and biomedicine, where the decision and statistical sciences play an increasingly important role.

Particular areas in which graduates can obtain employment or continue with graduate study include:

Students in this area study the mathematical theories of probability and statistics and their application to mathematical models that contain an element of uncertainty or randomness. Opportunities for employment are manifold in businesses and government agencies, and include a broad range of areas from the natural sciences, social sciences, and technology.  Concrete examples include pharmacology, genomics, medicine, environmental sciences, social network analysis, and information technology. 

Operations Research

In this area, students study mathematical, statistical, and computational techniques related to decision making. Operations research is crucial in business, government, and other management areas where decisions are made by solving large, complex problems (for example, crew scheduling for airlines, and the design of online recommendation systems). In addition to their major courses, students interested in this field are encouraged to take courses in business and economics.

Actuarial Science

Actuaries work primarily in businesses that involve financial risk, including the insurance industry. Students interested in this field take advanced courses in statistics, stochastic processes, and the mathematical theory of risk.

All majors and minors have a primary academic advisor in Steele Building. Students are strongly encouraged to meet regularly with their advisor and review their Tar Heel Tracker each semester. STAN majors and minors are also encouraged to meet with departmental advisors to discuss course planning before registration each semester. The director of undergraduate studies works with prospective majors and minors by appointment. Additional information on courses, undergraduate research opportunities, the honors program, careers, and graduate schools may be obtained from the department’s website or by contacting the director of undergraduate studies.

Courses for Students from Other Departments

The Department of Statistics and Operations Research offers a variety of courses of potential value to students majoring in other disciplines. Introductory courses include STOR 113 and STOR 215 , which are foundation courses in decision models, and the basic statistical courses, STOR 120  and STOR 155 . At the intermediate level, STOR 305 provides an introduction to business decision models, while STOR 320 is an introductory course to data science. Substantial coverage of applied statistical methods is provided in STOR 455 and STOR 556 . At more advanced mathematical levels, an introduction to probability theory is provided by STOR 435  (or  STOR 535 ), an introduction to proof techniques and discrete mathematics is given in STOR 315 , and the basic theory of statistical inference is given by STOR 555 . More advanced deterministic and stochastic models of operations research are provided in STOR 415 and STOR 445 . Machine learning is covered in STOR 565 and STOR 566 .

Graduate School and Career Opportunities

Regardless of the electives chosen, the statistics and analytics degree program provides excellent preparation for graduate study. Graduates with concentrations in operations research or statistics often continue work at the graduate level in those fields or related areas such as industrial engineering, biostatistics, and environmental science, or enter business school to pursue a master’s in business administration (M.B.A.) degree.

A five-year B.S.–M.S. degree program in statistics, operations research and data science is also an option. Interested students should consult the director of graduate studies for the operations research program.

Graduates of the statistics and analytics program will find numerous opportunities for well-paid, challenging jobs.

• Statistics and Analytics Major, B.S.
• Data Science Minor
• Statistics and Analytics Minor

Graduate Programs

• M.S. in Statistics and Operations Research
• Ph.D. in Statistics and Operations Research

Nilay Argon, Shankar Bhamidi, Amarjit Budhiraja, Jan Hannig, Vidyadhar G. Kulkarni, Yufeng Liu, James Stephen Marron, Andrew Nobel, Mariana Olvera-Cravioto, Gabor Pataki, Vladas Pipiras, Richard L. Smith, Serhan Ziya.

Associate Professors

Sayan Banerjee, Chuanshu Ji, Quoc Tran-Dinh, Kai Zhang.

Assistant Professors

Guanting Chen, Nicolas Fraiman, Xiangying Huang, Yao Li, Michael O'Neill, Zhengwu Zhang.

Teaching Associate Professor

Jeffrey McLean.

Teaching Assistant Professors

Oluremi Abayomi, Charles Dunn, Mario Giacomazzo, William Lassiter.

Joint Professors 

Joseph Ibrahim, Michael Kosorok, Jayashankar Swaminathan.

Professors Emeriti

George S. Fishman, Douglas G. Kelly, J. Scott Provan, David S. Rubin, Gordon D. Simons, Walter L. Smith, Shaler Stidham Jr., Jon W. Tolle.

STOR–Statistics and Operations Research

Undergraduate-level courses.

In this course, we will investigate the structure of these decision problems, show how they can be solved (at least in principle), and solve some simple problems.

Networks, mathematical structures that are composed of nodes and a set of lines joining the nodes, are used to model a wide variety of familiar systems.

This seminar aims to show that contrary to common belief, statistics can be exciting and fun. The seminar will consist of three modules: statistics in our lives, randomness, and principles of statistical reasoning.

The aim of this class is to study the role of uncertainty in our daily lives, to explore the cognitive biases that impair us, and to understand how one uses quantitative models to make decisions under uncertainty in a wide array of fields including medicine, law, finance, and the sciences.

This seminar will use recently assembled historical material to tell the exciting story of the origins and development of operations research during and after World War II.

We will study some basic statistical decision-making procedures and the errors and losses they lead to. We will analyze the effects of randomness on decision making using computer experimentation and physical experiments with real random mechanisms like dice, cards, and so on.

Studies the Environmental Protection Agency's Criteria Document, mandated by the Clean Air Act; this document reviews current scientific evidence concerning airborne particulate matter. Students learn some of the statistical methods used to assess the connections between air pollution and mortality, and prepare reports on studies covered in the Criteria Document.

The theory of probability, which can be used to model the uncertainty and chance that exist in the real world, often leads to surprising conclusions and seeming paradoxes. We survey and study these, along with other paradoxes and puzzling situations arising in logic, mathematics, and human behavior.

This course is designed to emphasize the motivation, philosophy, and cultivation of statistical reasoning in the interdisciplinary areas of statistical science and bioinformatics.

Introduces basic concepts in finance and economics, useful tools for collecting and summarizing financial data, and simple probability models for quantification of market uncertainty.

This seminar looks at a variety of ways in which modern computational tools allow easy and informative viewing of data. Students will also study the kinds of choices that have to be made in data presentation and viewing.

Introduces students to the world of genetics and DNA and to the use of computers to organize and understand the complex systems associated with the structure and dynamics of DNA and heredity.

Special Topics Course. Contents will vary each semester.

An introduction to multivariable quantitative models in economics. Mathematical techniques for formulating and solving optimization and equilibrium problems will be developed, including elementary models under uncertainty.

Students will use mathematical and statistical methods to address societal problems, make personal decisions, and reason critically about the world. Authentic contexts may include voting, health and risk, digital humanities, finance, and human behavior. This course does not count as credit towards the psychology or neuroscience majors.

The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

Elementary introduction to statistical reasoning, including sampling, elementary probability, statistical inference, and data analysis. STOR 151 may not be taken for credit by students who have credit for ECON 400 or PSYC 210 .

Data analysis; correlation and regression; sampling and experimental design; basic probability (random variables, expected values, normal and binomial distributions); hypothesis testing and confidence intervals for means, proportions, and regression parameters; use of spreadsheet software.

Examines selected topics from statistics and operations research. Course description is available from the department office.

Introduction to basic concepts and techniques of discrete mathematics with applications to business and social and physical sciences. Topics include logic, sets, functions, combinatorics, discrete probability, graphs, and networks.

This course introduces students to some of the key mathematical tools underlying algorithmic data science. The primary focus of the course is matrix algebra and multivariable calculus. The mathematical topics covered in the course will be motivated and connected by concrete applications in data science, with an emphasis on machine learning and optimization.

Experience includes preparations, demonstrations, assistance, and attendance at weekly meetings and lab sections. This course will enable you to deepen your understanding of topics in statistics and data science by learning the material with an eye toward explaining it to other less-experienced students, and; develop pedagogical skills, such as developing a rapport with learners, engaging in clear oral and written communication, and taking the perspective of less experienced students.

The use of mathematics to describe and analyze large-scale decision problems. Situations involving the allocation of resources, making decisions in a competitive environment, and dealing with uncertainty are modeled and solved using suitable software packages. Students cannot enroll in STOR 305 if they have already taken STOR 415 .

The course gives a solid introduction to rigorous mathematical thinking and problem solving, all of which are fundamental in data science. It covers proofs, mathematical induction, counting, and the basics of graph theory.

Development of basic skill set for data analysis from obtaining data to data carpentry, exploration, modeling, and communication. Topics covered include regression, clustering, classification, algorithmic thinking, and non-standard data objects (networks and text data). Students may not receive credit for both STOR 320 and STOR 520 .

Fundamental principles and methods of sampling populations, with emphasis on simple, random, stratified, and cluster sampling. Sample weights, nonsampling error, and analysis of data from complex designs are covered. Practical experience through participation in the design, execution, and analysis of a sampling project.

Advanced Undergraduate and Graduate-level Courses

Linear, integer, nonlinear, and dynamic programming, classical optimization problems, network theory.

Introduction to mathematical theory of probability covering random variables; moments; binomial, Poisson, normal and related distributions; generating functions; sums and sequences of random variables; and statistical applications. Students may not receive credit for both STOR 435 and STOR 535 .

Introduction to Markov chains, Poisson process, continuous-time Markov chains, renewal theory. Applications to queueing systems, inventory, and reliability, with emphasis on systems modeling, design, and control.

Review of basic inference; two-sample comparisons; correlation; introduction to matrices; simple and multiple regression (including significance tests, diagnostics, variable selection); analysis of variance; use of statistical software.

Probability models for long-term insurance and pension systems that involve future contingent payments and failure-time random variables. Introduction to survival distributions and measures of interest and annuities-certain.

Short term probability models for potential losses and their applications to both traditional insurance systems and conventional business decisions. Introduction to stochastic process models of solvency requirements.

This course will introduce students to the healthcare industry and provide hands-on experience with key actuarial and analytical concepts that apply across the actuarial field. Using real world situations, the course will focus on how mathematics and the principles of risk management are used to help insurance companies and employers make better decisions regarding employee benefit insurance products and programs.

Requires permission of the department. Statistics and analytics majors only. An opportunity to obtain credit for an internship related to statistics, operations research, or actuarial science. Pass/Fail only. Does not count toward the statistics and analytics major or minor.

Permission of the director of undergraduate studies. This course is intended mainly for students working on honors projects. May be repeated for credit.

An introduction to algorithms and modeling techniques that use knowledge gained from prior experience to make intelligent decisions in real time. Topics include Markov decision processes, dynamic programming, multiplicative weights update, exploration vs. exploitation, multi-armed bandits, and two player games.

This course provides hands-on experience working with data sets provided in class and downloaded from certain public websites. Lectures cover basic topics such as R programming, visualization, data wrangling and cleaning, exploratory data analysis, web scraping, data merging, predictive modeling, and elements of machine learning. Programming analyses in more advanced areas of data science. Students may not receive credit for both STOR 320 and STOR 520 .

This course is an advanced undergraduate course in probability with the aim to give students the technical and computational tools for advanced courses in data analysis and machine learning. It covers random variables, moments, binomial, Poisson, normal and related distributions, generating functions, sums and sequences of random variables, statistical applications, Markov chains, multivariate normal and prediction analytics. Students may not receive credit for both STOR 435 and STOR 535 .

This course will survey the history of sports analytics across multiple areas and challenge students in team-based projects to practice sports analytics. Students will learn how applied statistics and mathematics help decision makers gain competitive advantages for on-field performance and off-field business decisions.

Functions of random samples and their probability distributions, introductory theory of point and interval estimation and hypothesis testing, elementary decision theory.

This course covers the fundamental theory and methods for time series data, as well as related statistical software and real-world data applications. Topics include the autocorrelation function, estimation and elimination of trend and seasonality, estimation and forecasting procedures in ARMA models and nonstationary time series models.

The course covers advanced data analysis methods beyond those in STOR 455 and how to apply them in a modern computer package, specifically R or R-Studio which are the primary statistical packages for this kind of analysis. Specific topics include (a) Generalized Linear Models; (b) Random Effects; (c) Bayesian Statistics; (d) Nonparametric Methods (kernels, splines and related techniques).

Introduction to theory and methods of machine learning including classification; Bayes risk/rule, linear discriminant analysis, logistic regression, nearest neighbors, and support vector machines; clustering algorithms; overfitting, estimation error, cross validation.

Deep neural networks (DNNs) have been widely used for tackling numerous machine learning problems that were once believed to be challenging. With their remarkable ability of fitting training data, DNNs have achieved revolutionary successes in many fields such as computer vision, natural language progressing, and robotics. This is an introduction course to deep learning.

This upper-level-undergraduate and beginning-graduate-level course introduces the concepts of modeling, programming, and statistical analysis as they arise in stochastic computer simulations. Topics include modeling static and discrete-event simulations of stochastic systems, random number generation, random variate generation, simulation programming, and statistical analysis of simulation input and output.

STOR 612 consists of three major parts: linear programming, quadratic programming, and unconstrained optimization. Topics: Modeling, theory and algorithms for linear programming; modeling, theory and algorithms for quadratic programming; convex sets and functions; first-order and second-order methods such as stochastic gradient methods, accelerated gradient methods and quasi-Newton methods for unconstrained optimization.

STOR 614 consists of three major parts: Integer programming, conic programming, and nonlinear optimization. Topics: modeling, theory and algorithms for integer programming; second-order cone and semidefinite programming; theory and algorithms for constrained optimization; dynamic programming; networks.

Required preparation, advanced calculus. Lebesgue and abstract measure and integration, convergence theorems, differentiation. Radon-Nikodym theorem, product measures. Fubini theorems. Lp spaces.

Foundations of probability. Basic classical theorems. Modes of probabilistic convergence. Central limit problem. Generating functions, characteristic functions. Conditional probability and expectation.

The aim of this 3-credit graduate course is to introduce stochastic modeling that is commonly used in various fields such as operations research, data science, engineering, business, and life sciences. Although it is the first course in a sequence of three courses, it can also serve as a standalone introductory course in stochastic modeling and analysis. The course covers the following topics: discrete-time Markov chains, Poisson processes, and continuous-time Markov chains.

This 3-credit course is the second graduate-level course on stochastic modeling that expands upon the material taught in STOR 641 . The course covers the following topics: renewal and regenerative processes, queueing models, and Markov decision processes.

Required preparation, two semesters of advanced calculus. Probability spaces. Random variables, distributions, expectation. Conditioning. Generating functions. Limit theorems: LLN, CLT, Slutsky, delta-method, big-O in probability. Inequalities. Distribution theory: normal, chi-squared, beta, gamma, Cauchy, other multivariate distributions. Distribution theory for linear models.

Point estimation. Hypothesis testing and confidence sets. Contingency tables, nonparametric goodness-of-fit. Linear model optimality theory: BLUE, MVU, MLE. Multivariate tests. Introduction to decision theory and Bayesian inference.

Permission of the instructor. Basics of linear models: matrix formulation, least squares, tests. Computing environments: SAS, MATLAB, S+. Visualization: histograms, scatterplots, smoothing, QQ plots. Transformations: log, Box-Cox, etc. Diagnostics and model selection.

ANOVA (including nested and crossed models, multiple comparisons). GLM basics: exponential families, link functions, likelihood, quasi-likelihood, conditional likelihood. Numerical analysis: numerical linear algebra, optimization; GLM diagnostics. Simulation: transformation, rejection, Gibbs sampler.

Introduces students to modeling, programming, and statistical analysis applicable to computer simulations. Emphasizes statistical analysis of simulation output for decision-making. Focuses on discrete-event simulations and discusses other simulation methodologies such as Monte Carlo and agent-based simulations. Students model, program, and run simulations using specialized software. Familiarity with computer programming recommended.

The purpose of this course is to provide a strong foundation in computational skills needed for reproducible research in data science and statistics. Topics will include computational tools and programming skills to facilitate reproducibility, as well as procedures and methods for reproducible conclusions.

Permission of the department. Majors only. Individual reading, study, or project supervised by a faculty member.

Visit Program Website

318 Hanes Hall, CB# 3260

(919) 843-6024

Vladas Pipiras

Director of Undergraduate Studies

Mariana Olvera-Cravioto

[email protected]

Administrative Support Associate

[email protected]

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Operations research (OR) is the discipline of applying advanced analytical methods to help make better decisions. It uses mathematical modeling, analysis, and optimization in a holistic approach to improving our knowledge of systems and designing useful, efficient systems. Its applications range from engineering to management, and from industry to the public sector.

Operations research has helped advance the mathematics of optimization, applied probability, and statistics. OR researchers, collaborating with colleagues in related fields, have created innovative methods for pricing goods and services, and for marketing them. They have contributed to improving transportation, developing new financial instruments and auctions, and analyzing biological and medical information, as well as many more areas. In today's complex and interconnected world, the rigorous techniques and methodologies of operations research have become especially important aids to informed decision making.

The Operations Research Center (ORC) coordinates a PhD degree and SM degree in operations research, providing a strong background in OR theory as well as the practical techniques used in building models for a wide variety of applications.  In addition, the ORC, in collaboration with the Sloan School of Management, offers a specialized one-year Master of Business Analytics (MBAn) .

Founded as an interdepartmental program, the Operations Research Center has maintained its interdisciplinary roots. Its faculty comes from nine different departments at MIT, including the Sloan School of Management, five of the engineering departments, the Department of Mathematics, the Department of Economics, and the Department of Urban Studies and Planning.

For more information about the Operations Research Center and its degree programs, visit the website or contact Laura Rose , Room E40-107, 617-253-9303. Request information on the MBAn program via email .

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MSc Operations Research & Analytics

• Graduate taught
• Department of Mathematics
• Application code G2U1
• Starting 2024
• Home full-time: Open
• Overseas full-time: Open
• Location: Houghton Street, London

The MSc Operations Research & Analytics provides you with the skills needed to apply mathematical methods to real-world analytics problems faced by companies, governments, and other institutions.

With study in practice and theory, you will gain deep insight into analytics problems. On the practical side, you will learn how to model a range of real-world problems using optimisation, simulation, and statistics, with specialist software taught with accompanying computer lab sessions. On the theoretical side, you will learn to recognise canonical underlying mathematical problems, and how to solve them with state-of-the-art methods. You will also have the opportunity to undertake a Project in Operations Research & Analytics, working in a consultancy role in a host organisation, where you will turn a real problem faced by the organisation into a mathematical model whose solution provides tangible benefit. Alternatively, you may choose to write a dissertation, supervised by a faculty member. The programme is designed for students with strong quantitative backgrounds wishing to deepen and broaden their mathematical knowledge while gaining applicable skills in high demand in the marketplace.

Programme details

For more information about tuition fees and entry requirements, see the fees and funding and assessing your application sections.

Entry requirements

Minimum entry requirements for msc operations research & analytics.

An upper second class honours (2:1) degree in a relevant discipline (or equivalent). Students should normally have taken university courses including calculus, linear algebra, and statistics. Appropriate work experience will also be considered.

Competition for places at the School is high. This means that even if you meet our minimum entry requirement, this does not guarantee you an offer of admission.

If you have studied or are studying outside of the UK then have a look at our  Information for International Students  to find out the entry requirements that apply to you.

Assessing your application

We welcome applications from all suitably qualified prospective students and want to recruit students with the very best academic merit, potential and motivation, irrespective of their background.

We carefully consider each application on an individual basis, taking into account all the information presented on your application form, including your:

- academic achievement (including predicted and achieved grades) - statement of academic purpose - two academic references - CV

See further information on supporting documents

You may also have to provide evidence of your English proficiency, although you do not need to provide this at the time of your application to LSE.   See our English language requirements .

When to apply

Applications for this programme are considered on a rolling basis, meaning the programme will close once it becomes full. There is no fixed deadline by which you need to apply, however, to be considered for any LSE funding opportunity, you must have submitted your application and all supporting documents by the funding deadline. See the fees and funding section for more details. 

Fees and funding

Every graduate student is charged a fee for their programme.

The fee covers registration and examination fees payable to the School, lectures, classes and individual supervision, lectures given at other colleges under intercollegiate arrangements and, under current arrangements, membership of the Students' Union. It does not cover living costs or travel or fieldwork.

Tuition fees 2024/25 MSc Operations Research & Analytics

Home students: £29,472 Overseas students: £29,472

The Table of Fees shows the latest tuition amounts for all programmes offered by the School.

For this programme, the tuition fee is the same for all students regardless of their fee status. However any financial support you are eligible for will depend on whether you are classified as a home or overseas student, otherwise known as your fee status. LSE assesses your fee status based on guidelines provided by the Department of Education.

Further information about fee status classification.

Fee reduction

Students who completed undergraduate study at LSE and are beginning taught graduate study at the School are eligible for a  fee reduction  of around 10 per cent of the fee.

Scholarships and other funding

The School recognises that the  cost of living in London  may be higher than in your home town or country, and we provide generous scholarships each year to home and overseas students.

This programme is eligible for needs-based awards from LSE, including the  Graduate Support Scheme ,  Master's Awards , and  Anniversary Scholarships . 

Selection for any funding opportunity is based on receipt of an offer for a place and submitting a Graduate Financial Support application, before the funding deadline. Funding deadline for needs-based awards from LSE:  25 April 2024 .

This programme is also eligible for   Economic and Social Research Council (ESRC) funding  when you apply as part of a 1+3 research programme. Selection for the ESRC funding is based on receipt of an application for a place – including all ancillary documents, before the funding deadline.

Funding deadline for the ESRC funding:  15 January 2024.

In addition to our needs-based awards, LSE also makes available scholarships for students from specific regions of the world and awards for students studying specific subject areas.  Find out more about financial support.

Government tuition fee loans and external funding

A postgraduate loan is available from the UK government for eligible students studying for a first master’s programme, to help with fees and living costs. Some other governments and organisations also offer tuition fee loan schemes.

Find out more about tuition fee loans

Further information

Fees and funding opportunities

Information for international students

LSE is an international community, with over 140 nationalities represented amongst its student body. We celebrate this diversity through everything we do.  

If you are applying to LSE from outside of the UK then take a look at our Information for International students . 

1) Take a note of the UK qualifications we require for your programme of interest (found in the ‘Entry requirements’ section of this page). 

2) Go to the International Students section of our website. 

3) Select your country. 

4) Select ‘Graduate entry requirements’ and scroll until you arrive at the information about your local/national qualification. Compare the stated UK entry requirements listed on this page with the local/national entry requirement listed on your country specific page.

Programme structure and courses

You will take three compulsory courses and will choose courses from a range of options within the Department and across other relevant departments, including Management and Statistics. 

(* denotes half unit)  

Fundamentals of Operations Research * Introduces a range of Operations Research techniques including linear programming, the simplex method and duality, Markov chains, queueing theory and birth and death processes, inventory models and dynamic programming.

Modelling in Operations Research * Provides hands-on training in the art of converting real-world problems to optimisation and simulation models, inputting the models into specialist software, solving the optimisation problem or exercising the simulation model, and deriving applicable conclusions about the original problem.

Data Analysis and Statistical Methods * Studies common techniques of statistical inference, together with theoretical justification. The techniques are then applied to linear and logistic regression and basic time series models. Statistical software R constitutes an integral part of the course and provides hands-on experience of data analysis. 

Either Project in Operations Research & Analytics A project in a host organisation taking a consultancy role. Or Dissertation in Operations Research & Analytics An independent research project of 10,000 words on an approved topic of your choice.

Courses to the value of one and a half units from a range of options.

For the most up-to-date list of optional courses please visit the relevant School Calendar page .

You must note, however, that while care has been taken to ensure that this information is up to date and correct, a change of circumstances since publication may cause the School to change, suspend or withdraw a course or programme of study, or change the fees that apply to it. The School will always notify the affected parties as early as practicably possible and propose any viable and relevant alternative options. Note that the School will neither be liable for information that after publication becomes inaccurate or irrelevant, nor for changing, suspending or withdrawing a course or programme of study due to events outside of its control, which includes but is not limited to a lack of demand for a course or programme of study, industrial action, fire, flood or other environmental or physical damage to premises.

You must also note that places are limited on some courses and/or subject to specific entry requirements. The School cannot therefore guarantee you a place. Please note that changes to programmes and courses can sometimes occur after you have accepted your offer of a place. These changes are normally made in light of developments in the discipline or path-breaking research, or on the basis of student feedback. Changes can take the form of altered course content, teaching formats or assessment modes. Any such changes are intended to enhance the student learning experience. You should visit the School’s  Calendar , or contact the relevant academic department, for information on the availability and/or content of courses and programmes of study. Certain substantive changes will be listed on the  updated graduate course and programme information page.

Teaching and assessment

Contact hours and independent study.

Teaching will combine traditional lectures with seminars. Several of the courses, including two of the three compulsory ones, will involve training in a programming language or use of specialised computational tools. These parts of those courses will have accompanying computer lab sessions in which students will actively develop their programming skills by applying them to a range of problems in OR. Most courses on the degree are quantitative, but one optional course may, depending on your choice, study OR-related methods or applications from a qualitative perspective. During the summer, you are required to do either a project in Operations Research & Analytics or a Dissertation in Operations Research & Analytics. The project involves work in a host organisation (in business, government, health, or a social non-profit organisation), in a consultancy role, typically turning a real problem faced by the organisation into a mathematical model whose solution provides tangible benefit. You will be marked on a project report. The Dissertation requires study of an area of research, or an application of advanced techniques, and a report of findings. 

Within your programme you will take a number of courses, including half unit courses and full unit courses, to a total of 4 units. In half unit courses, on average, you can expect 35 contact hours in total and for full unit courses, 40-60 contact hours in total. This includes sessions such as lectures, seminars or workshops. Hours vary from course to course and you can view indicative details in the  Calendar  within the Teaching section of each  course guide .

You are also expected to complete independent study outside of class time. This requires you to manage the majority of your study time yourself, reading, thinking, solving problems, doing software exercise, and undertaking research.

Teaching methods

LSE is internationally recognised for its teaching and research and therefore employs a rich variety of teaching staff with a range of experience and status. Courses may be taught by members of faculty, such as assistant, associate, and full professors. Many departments now also employ guest teachers and visiting members of staff, LSE teaching fellows, and graduate teaching assistants who are usually doctoral research students and in the majority of cases teach on undergraduate courses only. You can view indicative details for the teacher responsible for each course in the relevant  course guide .

All taught courses are required to include formative coursework which is unassessed. It is designed to help prepare you for summative assessment which counts towards the course mark and to the degree award. LSE uses a range of formative assessment, such as essays, problem sets, case studies, reports, quizzes, mock exams and many others. Summative assessment may be conducted during the course or by final examination at the end of the course. An indication of the formative coursework and summative assessment for each course can be found in the relevant  course guide .

Academic support

You will also be assigned an academic mentor who will be available for guidance and advice on academic or personal concerns.

There are many opportunities to extend your learning outside the classroom and complement your academic studies at LSE.  LSE LIFE  is the School’s centre for academic, personal and professional development. Some of the services on offer include: guidance and hands-on practice of the key skills you will need to do well at LSE: effective reading, academic writing and critical thinking; workshops related to how to adapt to new or difficult situations, including development of skills for leadership, study/work/life balance and preparing for the world of work; and advice and practice on working in study groups and on cross-cultural communication and teamwork.

LSE is committed to enabling all students to achieve their full potential and the School’s  Disability and Wellbeing Service  provides a free, confidential service to all LSE students and is a first point of contact for all disabled students.

Student support and resources

We’re here to help and support you throughout your time at LSE, whether you need help with your academic studies, support with your welfare and wellbeing or simply to develop on a personal and professional level.

Whatever your query, big or small, there are a range of people you can speak to who will be happy to help.  

Department librarians   – they will be able to help you navigate the library and maximise its resources during your studies. 

Accommodation service  – they can offer advice on living in halls and offer guidance on private accommodation related queries.

Class teachers and seminar leaders  – they will be able to assist with queries relating to specific courses. 

Disability and Wellbeing Service  – they are experts in long-term health conditions, sensory impairments, mental health and specific learning difficulties. They offer confidential and free services such as  student counselling,  a  peer support scheme  and arranging  exam adjustments.  They run groups and workshops.  

IT help  – support is available 24 hours a day to assist with all your technology queries.   

LSE Faith Centre  – this is home to LSE's diverse religious activities and transformational interfaith leadership programmes, as well as a space for worship, prayer and quiet reflection. It includes Islamic prayer rooms and a main space for worship. It is also a space for wellbeing classes on campus and is open to all students and staff from all faiths and none.   

Language Centre  – the Centre specialises in offering language courses targeted to the needs of students and practitioners in the social sciences. We offer pre-course English for Academic Purposes programmes; English language support during your studies; modern language courses in nine languages; proofreading, translation and document authentication; and language learning community activities.

LSE Careers  ­ – with the help of LSE Careers, you can make the most of the opportunities that London has to offer. Whatever your career plans, LSE Careers will work with you, connecting you to opportunities and experiences from internships and volunteering to networking events and employer and alumni insights. 

LSE Library   –   founded in 1896, the British Library of Political and Economic Science is the major international library of the social sciences. It stays open late, has lots of excellent resources and is a great place to study. As an LSE student, you’ll have access to a number of other academic libraries in Greater London and nationwide. 

LSE LIFE  – this is where you should go to develop skills you’ll use as a student and beyond. The centre runs talks and workshops on skills you’ll find useful in the classroom; offers one-to-one sessions with study advisers who can help you with reading, making notes, writing, research and exam revision; and provides drop-in sessions for academic and personal support. (See ‘Teaching and assessment’). 

LSE Students’ Union (LSESU)  – they offer academic, personal and financial advice and funding.  

PhD Academy   – this is available for PhD students, wherever they are, to take part in interdisciplinary events and other professional development activities and access all the services related to their registration. 

Sardinia House Dental Practice   – this   offers discounted private dental services to LSE students.  

St Philips Medical Centre  – based in Pethwick-Lawrence House, the Centre provides NHS Primary Care services to registered patients.

Student Services Centre  – our staff here can answer general queries and can point you in the direction of other LSE services.  

Student advisers   – we have a  Deputy Head of Student Services (Advice and Policy)  and an  Adviser to Women Students  who can help with academic and pastoral matters.

Student life

As a student at LSE you’ll be based at our central London campus. Find out what our campus and London have to offer you on academic, social and career perspective. 

Student societies and activities

Your time at LSE is not just about studying, there are plenty of ways to get involved in  extracurricular activities . From joining one of over 200 societies, or starting your own society, to volunteering for a local charity, or attending a public lecture by a world-leading figure, there is a lot to choose from. 

The campus 

LSE is based on one  campus  in the centre of London. Despite the busy feel of the surrounding area, many of the streets around campus are pedestrianised, meaning the campus feels like a real community. 

Life in London 

London is an exciting, vibrant and colourful city. It's also an academic city, with more than 400,000 university students. Whatever your interests or appetite you will find something to suit your palate and pocket in this truly international capital. Make the most of career opportunities and social activities, theatre, museums, music and more. 

Want to find out more? Read why we think  London is a fantastic student city , find out about  key sights, places and experiences for new Londoners . Don't fear, London doesn't have to be super expensive: hear about  London on a budget . 

Student stories

To read all our Alumni Stories,  see our webpage here .

Philipp Loick - MSc Operations Research & Analytics 2017-18

Having a background in finance and economics, I aimed for a Masters programme where I could develop mathematical and programming skills to solve industry problems in operations research and data science. Enrolling in the Operations Research and Analytics programme at LSE was the right choice for this goal.

The programme features a diverse student body with the majority of students having majored in mathematics with some engineering and finance students. Even though only a one-year programme, the programme achieved a good balance between theoretical foundations and industry applications and allowed us to study topics such as combinatorial optimization, advanced statistics or algorithmic techniques for data mining.

The high academic level and relevance of the programme is due to the academic staff, who have excellent academic credentials, partially have worked for renowned industry companies and are well connected in the academic community. Graduating from the programme, I had an offer from BCG Gamma, the advanced analytics team of BCG, which I rejected for a PhD in discrete mathematics.

Alexander Saftschuk - MSc Operations Research & Analytics 2017-18

I came to the LSE with the main goal of improving my quantitative problem-solving skills, and subsequently landing a job in investment banking. The School and societies provided extremely good network opportunities, which really helped to land the job that I aimed at. After only two months at the LSE I landed a job offer with one of the top global investment banks. However, upon finishing the Operations Research & Analytics programme I quickly realised that I would rather pursue a career in data science, and once again the university's reputation opened doors for me last minute. Currently I work as a Data Analyst in the Telenor Digital data science team in Norway. There I code various machine learning algorithms in R, all of which I have all learned during this degree. 

Overall I can say that coming from a non-quantitative, business background I have learned more in this one-year Masters than I did in my entire three years of my bachelor degree. The programme was challenging but manageable. In particular, I highly appreciated how much face time I received from all of my professors, as well as the professor who supervised my thesis. The decision to come to the LSE and studying Operations Research & Analytics was one of the best I have made so far and I can highly recommend LSE and the degree. 

Kate Lavrinenko - MSc Operations Research & Analytics 2017-18

Studying this Masters was my third MSc, after studying Applied Mathematics and Economics, four years of experience in Economics and Finance, moving country, two kids, and four years at home with them. It was a challenging experience to find myself among young, inspired and able students from around the world. It also took some time to get used to the pace of study, and to network with people and share skills and knowledge. I needed some psychological help at the start of the journey and I had an opportunity to get it at LSE, which makes me feel grateful. 

I liked that the programme was flexible in what courses you could choose in order to make it fit your personal interests and academic goals. I encourage students to research and think hard about their course choices before starting the programme. Also, it is useful to have an understanding of which direction you wish to head in (e.g. academic or business) so you can utilise LSE’s resources properly. 

I found the careers events to be very valuable in my experience here. For example, I met a member of the Data Science team from Deloitte and after many rounds and following my MA425 Project there in the summer, I found myself with a full time job after finishing the course.

I enjoyed my journey, my job, and my experience with LSE. Whenever I get a new research heavy task, I start dreaming whether I could eventually turn it into a PhD, so my journey is not over.

Preliminary reading

You are not  required to do any preliminary reading in advance of this programme, but if you wish to read some material before arriving, we can make a few suggestions. 

If you do not have experience of  computer programming, you could learn the language R, which you will use in ST447 Data Analysis and Statistical Methods . Once you learn any language it is easy to learn others, and programming will be useful in your career. Programming will also give you a sense of what computers can and cannot do, that will be useful in all algorithmic courses. Good starting points are Introductory Statistics with R  by Peter Dalgaard, and the Coursera course .

Linear algebra plays a major role in several key courses and in the field of OR generally. It is expected that you are comfortable with the basic notions (linear independence, rank, determinants, solutions of systems of equations, eigenvalues and eigenvectors). These will not be reviewed in the course; you can review this material independently. There are many good textbooks to choose from; a suitable one is Linear Algebra by Martin Anthony and Michele Harvey. 

Quick Careers Facts for the Department of Mathematics

Median salary of our PG students 15 months after graduating: £39,500

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• Information, Digital Technology and Data            
• Accounting and Auditing              
• Real Estate, Environment and Energy 
• Advertising, Marketing, PR, Media, Entertainment, Publishing and Journalism

Top 5 sectors our students work in:

The data was collected as part of the Graduate Outcomes survey, which is administered by the Higher Education Statistics Agency (HESA). Graduates from 2020-21 were the fourth group to be asked to respond to Graduate Outcomes. Median salaries are calculated for respondents who are paid in UK pounds sterling and who were working in full-time employment.

This programme is ideal preparation for a range of careers in quantitative positions in consultancy, management, finance, government and business, anywhere in the world.

Further information on graduate destinations for this programme

Support for your career

Many leading organisations give careers presentations at the School during the year, and LSE Careers has a wide range of resources available to assist students in their job search. Find out more about the  support available to students through LSE Careers .

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Webinars, videos, student blogs and student video diaries will help you gain an insight into what it's like to study at LSE for those that aren't able to make it to our campus.  Experience LSE from home . 

Come on a guided campus tour, attend an undergraduate open day, drop into our office or go on a self-guided tour.  Find out about opportunities to visit LSE . 

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Student Marketing, Recruitment and Study Abroad travels throughout the UK and around the world to meet with prospective students. We visit schools, attend education fairs and also hold Destination LSE events: pre-departure events for offer holders.  Find details on LSE's upcoming visits . 

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• Operations Research Analysts: Jobs, Career, Salary and Education Information

Operations Research Analysts

Career, salary and education information.

What They Do : Operations research analysts use advanced mathematical and analytical methods to help solve complex issues.

Work Environment : Operations research analysts spend most of their time in offices, although some travel may be necessary to meet with clients. Almost all operations research analysts work full time.

How to Become One : Although the typical educational requirement for entry-level positions is a bachelor’s degree, some employers may prefer to hire applicants with a master’s degree. Analysts typically have a degree in business, operations research, management science, analytics, mathematics, engineering, computer science, or another technical or quantitative field.

Salary : The median annual wage for operations research analysts is $82,360.

Job Outlook : Employment of operations research analysts is projected to grow 23 percent over the next ten years, much faster than the average for all occupations.

Related Careers : Compare the job duties, education, job growth, and pay of operations research analysts with similar occupations.

Following is everything you need to know about a career as an operations research analyst with lots of details. As a first step, take a look at some of the following jobs, which are real jobs with real employers. You will be able to see the very real job career requirements for employers who are actively hiring. The link will open in a new tab so that you can come back to this page to continue reading about the career:

Top 3 Operations Research Analyst Jobs

Operations Research Analyst Luminocity Inc. in Hicksville, New York is seeking a full-time Operations Research Analyst .The job responsibilities include collecting and organizing data from sales ...

The Operations Research Analyst will provide support in the analysis of business requirements. The Operations Research Analyst responsibilities will include: * Providing systems analysis support to ...

CTI is seeking a Operations Research Systems Analyst (ORSA)/ Data Scientists to join our team. As an Operations Research and Systems Analyst (ORSA) you will support a Department of Defense (DoD ...

See all Operations Research Analyst jobs

What Operations Research Analysts Do [ About this section ] [ To Top ]

Operations research analysts use advanced mathematical and analytical methods to help organizations solve problems and make better decisions.

Duties of Operations Research Analysts

Operations research analysts typically do the following:

• Identify and solve problems in areas such as business, logistics, healthcare, or other fields
• Collect and organize information from a variety of sources, such as computer databases, sales histories, and customer feedback
• Gather input from workers involved in all aspects of a problem or from others who have specialized knowledge, so that they can help solve the problem
• Examine information to figure out what is relevant to a problem and what methods might be used to analyze it
• Use statistical analysis, simulations, predictive modeling, or other methods to analyze information and develop practical solutions to business problems
• Advise managers and other decisionmakers on the effects of various courses of action to take in order to address a problem
• Write memos, reports, and other documents explaining their findings and recommendations for managers, executives, and other officials

Operations research analysts are involved in all aspects of an organization. They help managers decide how to allocate resources, develop production schedules, manage the supply chain, and set prices. For example, they may help decide how to organize products in supermarkets or help companies figure out the most effective way to ship and distribute products.

Analysts must first identify and understand the problem to be solved or the processes to be improved. Analysts typically collect relevant data from the field and interview clients or managers involved in the business processes being examined. Analysts show the implications of pursuing different actions and may assist in achieving a consensus on how to proceed.

Operations research analysts use sophisticated computer software, such as databases and statistical packages, to analyze and solve problems. Analysts use statistical software to simulate current and future events and evaluate alternative courses of action. Analysts break down problems into their various parts and analyze the effect that different changes and circumstances would have on each of these parts. For example, to help an airline schedule flights and decide what to charge for tickets, analysts may take into account the cities that have to be connected, the amount of fuel required to fly those routes, the expected number of passengers, pilots' schedules, maintenance costs, and fuel prices.

There is no one way to solve a problem, and analysts must weigh the costs and benefits of alternative solutions or approaches in their recommendations to managers.

Because problems are complex and often require expertise from many disciplines, most analysts work on teams. Once a manager reaches a final decision, these teams may work with others in the organization to ensure that the plan is successful.

Work Environment for Operations Research Analysts [ About this section ] [ To Top ]

Operations research analysts hold about 104,200 jobs. The largest employers of operations research analysts are as follows:

Some operations research analysts in the federal government work for the Department of Defense, which also employs analysts through private consulting firms.

Operations research analysts spend much of their time in office settings. They may travel to gather information, observe business processes, work with clients, or attend conferences.

Operations Research Analyst Work Schedules

Most operations research analysts work full time.

How to Become an Operations Research Analyst [ About this section ] [ To Top ]

Get the education you need: Find schools for Operations Research Analysts near you!

Although the typical educational requirement for entry-level positions is a bachelor's degree, some employers may prefer to hire applicants with a master's degree. Because few schools offer bachelor's and advanced degree programs in operations research, analysts typically have degrees in other related fields.

Education for Operations Research Analysts

Many entry-level positions are available for those with a bachelor's degree. However, some employers may prefer to hire applicants with a master's degree.

Although some schools offer bachelor's and advanced degree programs in operations research, some analysts have degrees in other technical or quantitative fields, such as engineering, computer science, analytics, or mathematics.

Because operations research is based on quantitative analysis, students need extensive coursework in mathematics. Courses include statistics, calculus, and linear algebra. Coursework in computer science is important because analysts rely on advanced statistical and database software to analyze and model data. Courses in other areas, such as engineering, economics, and political science, are useful because operations research is a multidisciplinary field with a wide variety of applications.

Continuing education is important for operations research analysts. Keeping up with advances in technology, software tools, and improved analytical methods is vital.

Other Experience for Operations Research Analysts

Some operations research analysts are veterans of the U.S. Armed Forces. Certain positions may require applicants to undergo a background check in order to obtain a security clearance.

Important Qualities for Operations Research Analysts

Analytical skills. Operations research analysts use a wide range of methods, such as forecasting, data mining, and statistical analysis, to examine and interpret data. They must determine the appropriate software packages and understand computer programming languages to design and develop new techniques and models.

Communication skills. Operations research analysts often present their data and conclusions to managers and other executives. They also need to communicate technical information to people without a technical background.

Critical-thinking skills. Operations research analysts must be able to figure out what information is relevant to their work. They also must be able to evaluate the costs and benefits of alternative solutions before making a recommendation.

Interpersonal skills. Operations research analysts typically work on teams. They also need to be able to convince managers and top executives to accept their recommendations.

Math skills. The models and methods used by operations research analysts are rooted in statistics, calculus, linear algebra, and other advanced mathematical disciplines.

Problem-solving skills. Operations research analysts need to be able to diagnose problems on the basis of information given to them by others. They then analyze relevant information to solve the problems.

Writing skills. Operations research analysts write memos, reports, and other documents explaining their findings and recommendations.

Operations Research Analyst Salaries [ About this section ] [ More salary/earnings info ] [ To Top ]

The median annual wage for operations research analysts is $82,360. The median wage is the wage at which half the workers in an occupation earned more than that amount and half earned less. The lowest 10 percent earned less than $48,690, and the highest 10 percent earned more than $160,850.

The median annual wages for operations research analysts in the top industries in which they work are as follows:

Job Outlook for Operations Research Analysts [ About this section ] [ To Top ]

Employment of operations research analysts is projected to grow 23 percent over the next ten years, much faster than the average for all occupations.

About 10,300 openings for operations research analysts are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

Employment of Operations Research Analysts

As technology advances and companies and government agencies seek efficiency and cost savings, demand for operations research analysis should continue to grow. In addition, increasing demand should occur for these workers in the field of analytics to improve business planning and decision making.

Technological advances have made it faster and easier for organizations to get data. Operations research analysts manage and evaluate data to improve business operations, supply chains, pricing models, and marketing. In addition, improvements in analytical software have made operations research more affordable and applicable to a wider range of areas. More companies are expected to employ operations research analysts to help them turn data into information that managers use to make decisions about all aspects of their business.

Careers Related to Operations Research Analysts [ About this section ] [ To Top ]

Data scientists.

Data scientists use analytical tools and techniques to extract meaningful insights from data.

Economists study the production and distribution of resources, goods, and services by collecting and analyzing data, researching trends, and evaluating economic issues.

Industrial Engineers

Industrial engineers find ways to eliminate wastefulness in production processes. They devise efficient systems that integrate workers, machines, materials, information, and energy to make a product or provide a service.

Logisticians

Logisticians analyze and coordinate an organization's supply chain—the system that moves a product from supplier to consumer. They manage the entire life cycle of a product, which includes how a product is acquired, allocated, and delivered.

Management Analysts

Management analysts, often called management consultants, propose ways to improve an organization's efficiency. They advise managers on how to make organizations more profitable through reduced costs and increased revenues.

Market Research Analysts

Market research analysts study market conditions to examine potential sales of a product or service. They help companies understand what products people want, who will buy them, and at what price.

Mathematicians and Statisticians

Mathematicians and statisticians analyze data and apply mathematical and statistical techniques to help solve real-world problems in business, engineering, healthcare, or other fields.

Software Developers

Software developers are the creative minds behind computer programs. Some develop the applications that allow people to do specific tasks on a computer or another device. Others develop the underlying systems that run the devices or that control networks.

More Operations Research Analyst Information [ About this section ] [ To Top ]

For more information about operations research analysts, visit

Institute for Operations Research and the Management Sciences

Military Operations Research Society

A portion of the information on this page is used by permission of the U.S. Department of Labor.

Explore more careers: View all Careers or the Top 30 Career Profiles

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How to Become an Operations Research Analyst

By Ibrahim Okunade

Published: March 25, 2024

Intrigued by numbers, problem-solving, and optimizing processes to make impactful decisions?

If your answer to this question is yes, the role of an operations research analyst might perfectly suit you. This guide explores the data-driven world of operations research analysts, diving into their diverse skill sets, the industries they serve, and the potential career opportunities available.

Career Summary

Operations research analyst salary.

Variables like an analyst’s level of education, years of experience, geographic location, industry, and the size and reputation of the employing organization affect the salary of research analysts.

As per Glassdoor , the salary breakdown for operations research analysts is as follows:

• Entry Salary (US$75k)
• Median Salary (US$95k)
• Executive Salary (US$121k

Operations research analysts surely belong to the category of high-income earners , considering the fact that the national average income for US citizens is $61,900 .

Operations Research Analyst Job Description

An operations research analyst is responsible for using advanced analytical techniques to solve complex problems and optimize processes within various industries. Their primary task involves collecting and analyzing data, formulating mathematical models, and applying optimization methods to provide data-driven insights and recommendations.

By identifying inefficiencies and proposing improvements, operations research analysts play a crucial role in enhancing decision-making, streamlining operations, and maximizing resource utilization.

Operations Research Analyst Career Progression

• Entry-Level Operations Research Analyst: Assists senior analysts, handles basic research, and performs statistical analyses.
• Junior Operations Research Analyst: Takes on more responsibilities, working independently on smaller projects. They develop a deeper understanding of various optimization techniques and may contribute to designing and implementing analytical models.
• Senior Operations Research Analyst: Takes on more complex and strategic projects. They play a lead role in analyzing data, developing sophisticated mathematical models, and providing key insights to decision-makers.
• Lead or Principal Operations Research Analyst: Leads larger projects and oversees multiple initiatives. They collaborate closely with stakeholders from different departments to identify optimization opportunities and align solutions with organizational objectives. Lead analysts are key contributors to shaping the analytical direction of their organizations.
• Operations Research Manager or Director: Responsible for managing a team of analysts and overseeing the execution of projects. They also play a significant role in setting the overall analytical strategy and driving innovation within the organization.

• Opportunity to work in diverse industries.
• Continuous learning opportunities.
• The field offers highly competitive salaries.
• Multiple opportunities for career advancement.
• The field has a positive job outlook.
• Balancing multiple projects simultaneously can be demanding.
• Challenging communication with non-technical stakeholders.
• Continuous need to update skills due to rapidly evolving technology.
• Dealing with complex and ambiguous data.
• Occasional resistance to data-driven decision-making culture.

Useful Skills to Have as an Operations Research Analyst

• Mathematical Modeling
• Data and Statistical Analysis
• Optimization Techniques
• Decision Analysis
• Communication Skills
• Project Management

Popular Operations Research Analyst Specialties

• Supply Chain Optimization
• Revenue Management
• Healthcare Analytics
• Financial Modeling and Risk Analysis
• Decision Support Systems
• Market Research and Forecasting

How to become an Operations Research Analyst

Complete Your Education

The first step in your operations research analyst journey is to complete your education.

You can start by earning a bachelor’s degree in operations research or other relevant fields, such as data science, mathematics, or a related discipline. The specific coursework you take will depend on the program you are enrolled in. However, most programs will include courses in mathematics, statistics, computer science, and operations research.

Do I Need a Degree to Become an Operations Research Analyst?

Yes, you need a degree to become an operations research analyst . In most cases, a bachelor’s degree in operations research and other relevant fields is the barest minimum, as some job openings require applicants to possess graduate qualifications.

Some specialized roles may require a master’s degree or even a Ph.D. in operations research, data science, or business analytics for more.

How Long Does it Take to Get a Degree in Operations Research?

A student’s individual circumstances and the level of degree are some factors that impact the duration it takes to get a degree. The same holds true for operations research programs.

Here is a breakdown of the expected timeframe it takes to complete different types of operations research degrees:

• Bachelor’s Degree: A bachelor’s degree in operations research usually takes four years to complete . Students typically need to complete around 120 to 130 credit hours of coursework , which includes general education requirements, core Operations Research courses, and elective courses.
• Master’s Degree: A Master’s degree in Operations Research usually takes around two years to complete . The duration may vary based on whether the program is full-time or part-time. Master’s programs typically require 50 to 70 credit hours of coursework , including advanced operations research topics and potentially a thesis or capstone project.
• Ph.D. Degree: Pursuing a Ph.D. in Operations Research is a more research-intensive path and can take anywhere from four to six years or more to complete . The duration depends on factors such as the individual’s research progress and the complexity of the dissertation. Ph.D. programs typically involve coursework, comprehensive exams, and extensive research leading to the completion of a doctoral dissertation.

How Much Does it Cost to Get a Degree in Operations Research?

A student’s residency status (in-state vs. out-of-state), type of school (public vs. private), and degree type are some of the factors that determine the cost of getting your degree in operations research. Thus, the cost is not fixed.

According to College Tuition Compare , in-state students studying for undergraduate degrees could pay as low as $13,319 for their tuition and fee. The fee could be as high as $51,100 for out-of-state students. The tuition and fees for students pursuing graduate degrees in-state cost as low as $14,220. Out-of-state students could pay as much as $35,980 for their graduate degree in operations research.

It is equally important to factor in additional costs like the cost of living, textbooks, and other miscellaneous resources.

Can I Become an Operations Research Analyst Through Online Education?

Yes, you can become an operations research analyst through online education . Online education has evolved significantly, and many reputable universities now offer fully accredited online programs in fields like operations research, data science, mathematics, and related disciplines. These online programs provide a flexible and convenient way for individuals to acquire the necessary skills and knowledge required for a career as an operations research analyst.

What are Some Web Resources to Learn Skills to Become an Operations Research Analyst?

As a data-driven field, things evolve and change quickly in the field of operations research. This is why it is important to keep up with new developments through digital channels. Several web resources offer valuable courses, tutorials, and materials to learn the skills needed to improve as an operations research analyst. These resources cover topics such as optimization techniques, mathematical modeling, data analysis, and more.

Here are some reputable web resources to get you started:

• INFORMS (Institute for Operations Research and the Management Sciences) : I NFORMS offers various resources, including webinars, tutorials, and conference presentations, which can be valuable for learning about the latest advancements and applications in operations research.
• The Operational Research Society : The Operation Research Society is a community that supports professional operational researchers across industries and academia. The website helps operations research analyst broaden their knowledge and also helps them stay updated with current trends in the field.
• Analytics Vidhya : While not specifically focused on operations research, Analytics Vidhya offers a vast collection of tutorials, articles, and resources on data science, machine learning, and optimization techniques relevant to operations research analysts.
• O’Reilly Data Show Podcast : The O’Reilly Data Show Podcast explores the opportunities and techniques driving big data and data science. It is useful to both aspiring and experienced data professionals, providing valuable insights that inspire innovation and problem-solving. Through in-depth interviews with leading experts and researchers, the podcast offers diverse perspectives and approaches to tackling complex data challenges.

Complete Additional Training

Operations research analysts need to be proficient in quantitative analysis, mathematical modeling, statistical methods, and data analysis. Learn data analysis techniques and programming languages commonly used in the field, such as Python , R , or MATLAB . Proficiency in these tools allows you to work with large datasets, clean data, and perform statistical analysis.

You should also familiarize yourself with optimization methods like linear programming, integer programming, dynamic programming, and other algorithms used to optimize systems and processes.

Gain Practical Experience

With the array of skills learned so far, the next step is to try your hands on real-life projects. There are two major ways to do this. You can either seek internship positions or work on research projects related to operations research. You can do this during your academic years or while transitioning into the field professionally.

Research projects can be an excellent way to deepen your understanding of specific operations research methodologies and explore niche areas within the field. Collaborating with professors or industry mentors on research initiatives hones your analytical abilities and equips you with the experience of formulating research questions, conducting experiments, and interpreting results.

This practical experience exposes you to real-world problem-solving, allowing you to apply your analytical skills in practical scenarios and work with actual data.

What Are Internship Opportunities for an Operations Research Analyst?

Internships provide valuable hands-on experience, exposure to real-world problem-solving, and an opportunity to showcase your skills to potential employers. They can be a significant stepping stone to launch your career as an operations research analyst and pave the way for future job opportunities within your preferred industry or sector.

Internship opportunities for an operations research analyst can be found in various industries and organizations that require analytical problem-solving and optimization skills. This includes consulting, technology, government, finance, manufacturing, retail , transportation, and healthcare.

During these internships, you could be involved in various tasks, such as data analysis, strategic planning, financial modeling, production optimization, supply chain management, or patient care process enhancement.

When searching for internships , utilize job platforms, career websites, and your university’s resources. Networking, both in-person and online, can uncover valuable opportunities. If you’re interested in a specific organization, don’t hesitate to contact them directly. Before applying, tailor your resume to the role and create a compelling cover letter.

Remember, the goal of an internship is not just to get work experience, but to learn and grow in your chosen field. Look for opportunities that align with your career goals and interests.

What Skills Will I Learn as an Operations Research Analyst?

As an operations research analyst, you gain a versatile skill set to expertly analyze data, optimize processes, and provide valuable insights for informed decision-making. This role nurtures diverse competencies vital for addressing complex challenges and driving efficiency across different domains.

Here are some key skills you will learn and enhance in this role:

• Mathematical Modeling and Optimization Techniques: You will learn how to construct mathematical models to represent real-world problems, whether they involve optimizing resources, scheduling tasks, or allocating budgets. You will also learn various optimization methods, such as linear programming, integer programming, dynamic programming, and heuristic algorithms, to find the best solutions to complex problems.
• Data Analysis and Interpretation: Analyzing and interpreting data is a core aspect of the role. You will learn how to work with data, clean it, and extract valuable insights to support decision-making.
• Decision Analysis: Operations research analysts assess and evaluate potential decisions under uncertainty. You will learn how to apply decision theory and risk analysis to make informed choices.
• Computer Programming : Learning programming languages like Python, R, or MATLAB will allow you to implement and automate your analytical models and conduct data analysis efficiently. In addition, familiarity with specialized software and tools used in Operations Research, such as Gurobi , CPLEX , or Excel Solver , is crucial for effective analysis and optimization.
• Quantitative Problem-Solving: You will become adept at tackling complex problems and breaking them down into solvable components, applying quantitative and analytical methods to reach optimal solutions.
• Communication Skills: While your core skills help you to tackle complex problems, your communication skills will help you present the information clearly. Therefore, operations research analysts must be able to effectively communicate their findings and recommendations to both technical and non-technical stakeholders.
• Project Management: In some cases, operations research analysts work on projects from conception to implementation. You will gain project management skills to coordinate and execute analytical projects effectively.
• Critical Thinking: Critical thinking is an important skill for an operations research analyst. Developing strong critical thinking abilities allows you to approach problems from various angles and devise innovative solutions.

Balancing Work and Life as an Operations Research Analyst

The work-life balance of operations research analysts can differ based on various factors. They typically work in office settings, and some may have the option to work remotely, which could provide a better work-life balance. However, their work-life balance can fluctuate depending on project demands. During busy periods or tight deadlines, they might need to work extra hours to complete tasks, but they may experience more flexibility when projects are less intense.

The industry and sector they work in also influence their work-life balance. Some industries may have busier periods, while others may offer more predictable schedules.

The workload and company culture also plays a significant role. Organizations that prioritize employee well-being may offer more flexibility and benefits promoting work-life balance. The level of autonomy and time management skills can also affect how much control they have over their work-life balance.

Experience and career level matter too. Junior analysts may have more structured schedules and limited decision-making authority, while senior-level analysts with more experience may enjoy a bit more autonomy.

Overall, achieving a satisfactory work-life balance is possible for operations research analysts, provided they prioritize their well-being and work in organizations with a positive work culture.

Earn Additional Certifications (optional)

While not always mandatory, obtaining additional certifications can be beneficial for operations research analysts. These certifications can enhance their skills, demonstrate expertise in specific areas, and make them more competitive in the job market. The relevance and necessity of certifications depend on the industry, job requirements, and individual career goals.

Here are some certifications that operations research analysts may consider:

• Certified Analytics Professional (CAP) : Offered by the Institute for Operations Research and the Management Sciences (INFORMS), CAP certification validates expertise in analytics and demonstrates proficiency in data-driven decision-making.
• Certified Data Professional (CDP) : Offered by the Institute for Certification of Computing Professionals (ICCP), this certification validates expertise in data management and data governance.
• Six Sigma Certifications : Six Sigma is a quality improvement methodology that uses statistical methods to identify and eliminate defects in processes. It is a valuable tool for operations research analysts because it can help them to improve the efficiency and effectiveness of their organizations.

Before pursuing any certification, you should assess your career goals, the industry’s demand for specific certifications, and how the certification aligns with your skill set. Additionally, some employers may offer support or incentives for obtaining certifications, so it’s worth considering the potential benefits both for professional development and career advancement.

What’s the Career Outlook for Operations Research Analysts?

As per the U.S. Bureau of Labor Statistics , there’s a promising forecast for operations research analysts, with a projected job growth of 23% between 2021 and 2031. This expansion rate significantly surpasses the average for all other U.S. occupations. Moreover, it’s estimated that about 10,300 new opportunities for operations research analysts will emerge annually over this ten-year period.

This reflects a robust job market and ample opportunities for individuals seeking to enter or advance in the field of operations research. The increased reliance on data-driven decision-making and the need to optimize processes across various industries are driving the demand for operations research analysts.

As organizations strive to enhance efficiency and make well-informed choices, skilled analysts who can provide valuable insights through data analysis and optimization techniques are highly sought after.

With such positive job prospects and a diverse range of industries to choose from, aspiring operations research analysts can look forward to a rewarding and promising career path in the coming years.

What are the Job Opportunities for an Operations Research Analyst?

Operations research analysts have a wide range of job opportunities across various industries. Their expertise in analyzing data, optimizing processes, and providing valuable insights makes them valuable assets in different domains.

Here are some common job opportunities for operations research analysts:

• Supply Chain Analyst: Supply chain analysts work on optimizing supply chain operations, including inventory management, distribution, and logistics, to enhance efficiency and reduce costs.
• Financial Analyst : Operations research analysts in finance focus on portfolio optimization, risk management, and investment decision-making using mathematical modeling and statistical analysis.
• Healthcare Analyst: In the healthcare sector, analysts use operations research techniques to optimize patient flow, resource allocation, and healthcare delivery processes.
• Marketing Analyst: Marketing analysts leverage data analysis and optimization methods to improve marketing campaigns, customer segmentation, and pricing strategies.
• Transportation Analyst: Transportation analysts focus on optimizing transportation routes, scheduling, and logistics to enhance transportation efficiency and reduce expenses.
• Government Analyst: Operations research analysts in government agencies work on policy analysis, resource allocation, and decision-making to improve public services and operations.
• Energy Analyst: In the energy sector, analysts use operations research techniques to optimize energy distribution, resource planning, and demand forecasting.
• Quality Analyst: Quality analysts use operations research techniques to optimize quality control processes and improve product or service quality.
• Revenue Management Analyst : Revenue management analysts focus on optimizing pricing and revenue strategies for businesses in industries like airlines and hospitality.
• Risk Analyst: Risk analysts use operations research methods to assess and manage risks in various industries, including finance and insurance.
• Environmental Analyst: Environmental analysts apply operations research techniques to address environmental challenges and optimize sustainability efforts.

Their versatile skill set allows operations research analysts to contribute to diverse sectors and tackle complex challenges across industries. Their ability to make data-driven decisions and improve efficiency makes them valuable assets in today’s data-centric and highly competitive business landscape.

What Type of Organizations Hire Operations Research Analysts?

Operations research analysts are sought after by a wide range of organizations that value data-driven decision-making, process optimization, and problem-solving. They are crucial in improving efficiency, reducing costs, and enhancing decision-making in various industries. So, what type of organizations can you work in as an operations research analyst?

Here are some of them:

• Consulting Firms: Management and strategy consulting firms hire operations research analysts to provide data-driven insights and optimize processes for their clients across different industries.
• Technology Companies: Technology companies use operations research analysts to optimize algorithms, improve user experiences, and enhance various operations, such as supply chain management and resource allocation.
• Manufacturing and Industrial Companies: Manufacturing and industrial organizations employ operations research analysts to optimize production processes, inventory management, and distribution networks.
• Financial Institutions: Banks, investment firms, and insurance companies hire these professionals to improve risk management, portfolio optimization, fraud detection, and customer analytics.
• Healthcare Organizations: Hospitals, healthcare providers, and pharmaceutical companies utilize operations research analysts to optimize patient flow, resource allocation, and healthcare delivery.
• Government Agencies: Federal, state, and local government agencies employ operations research analysts for policy analysis, resource allocation, and process optimization in various public services.
• Transportation and Logistics Companies: Transportation companies, logistics providers, and airlines need the expertise of operations research analysts to optimize routes, schedules, and fleet management.
• Retail and E-commerce Companies: Retailers and e-commerce platforms also need the expertise of operations research analysts to optimize inventory management, pricing strategies, and supply chain operations.
• Energy and Utility Companies: Energy providers and utilities employ operations research analysts to optimize energy distribution, resource planning, and demand forecasting.
• Aerospace and Defense Companies: Aerospace and defense organizations utilize Operations research analysts to optimize complex projects, resource allocation, and logistics.

In addition to these organizations, operations research analysts also work in academia. They are typically suited to roles that require a holistic analysis of data to make decisions.

Should I become an Operations Research Analyst?

Whether or not you should become an operations research analyst is a personal decision. However, if you are considering this career path, you should peruse the information in this guide and assess a typical operations research analyst job description to understand the requirements of the job.

Operations research analysts use mathematical models and statistical analysis to solve complex problems in different industries. They work with data to identify inefficiencies and develop solutions that improve efficiency and effectiveness. The job of an operations research analyst can be challenging and demanding, but it can also be very rewarding. If you are interested in a career that combines analytical thinking, problem-solving, and creativity, then operations research may be a good fit for you.

Finally, explore the industries and organizations that hire operations research analysts. This will give you an idea of the diverse opportunities available and the potential for growth and career advancement.

Careers Related to Operations Research Analyst

• Business Analyst
• Data Analyst
• Financial Analyst
• Management Analyst
• Statistician

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Operations Research Vs Data Science: A Detailed Comparison

Operations research and data science are two fields that leverage analytical methods and statistical modeling to solve complex business problems and drive data-driven decision making. On the surface, they may seem quite similar – but there are some key differences between these disciplines that are important to understand.

If you’re short on time, here’s a quick answer: Operations research focuses on optimizing business operations and resources through quantitative analysis and mathematical modeling . Data science leverages statistical methods and machine learning to extract insights and predict outcomes from large, complex datasets.

Defining Operations Research

Operations Research (OR) is a field of study that combines mathematical modeling, statistical analysis, and optimization techniques to solve complex problems in various industries. It involves applying quantitative methods to decision-making processes, with the aim of improving operational efficiency and effectiveness.

Mathematical modeling and optimization

One of the key aspects of Operations Research is the use of mathematical models to represent real-world systems. These models help in understanding the underlying processes and identifying the most efficient ways to allocate resources, schedule tasks, and make decisions.

Optimization techniques are then applied to these models to find the best possible solutions, considering various constraints and objectives.

Focus on operational efficiency

Operations Research primarily focuses on improving operational efficiency, which involves maximizing output while minimizing costs, time, and other resources. By analyzing data and making informed decisions, OR professionals strive to optimize processes, improve productivity, and reduce wastage.

This can lead to significant cost savings and increased profitability for businesses.

Prescriptive analytics

Prescriptive analytics is a branch of Operations Research that goes beyond descriptive and predictive analytics. While descriptive analytics focuses on analyzing historical data, and predictive analytics aims to forecast future outcomes, prescriptive analytics provides recommendations on the best course of action to achieve specific goals.

It takes into account multiple scenarios, constraints, and objectives to provide decision-makers with actionable insights.

Operations Research is a multidisciplinary field that draws from mathematics, statistics, computer science, economics, and other disciplines. It has applications in a wide range of industries, including logistics, supply chain management, healthcare, finance, and transportation.

By using advanced analytical techniques, operations researchers help organizations make data-driven decisions and improve overall performance.

Defining Data Science

Data Science is a multidisciplinary field that combines various techniques and methodologies to extract valuable insights and knowledge from vast amounts of data. It involves the use of statistical analysis, machine learning, and predictive analytics to uncover patterns, trends, and relationships within datasets.

Data Scientists employ various tools and programming languages such as Python and R to manipulate and analyze data, and they often work with large datasets that require advanced computational skills.

Statistical analysis and machine learning

Statistical analysis plays a crucial role in data science as it helps in understanding the underlying patterns and relationships within the data. Data Scientists use statistical techniques to identify correlations, test hypotheses, and make predictions based on the data.

They also employ machine learning algorithms to develop models that can automatically learn from data and make predictions or decisions without being explicitly programmed.

Machine learning algorithms are designed to learn from data and improve their performance over time. They can be used to solve a wide range of problems, such as image recognition, natural language processing, and recommendation systems.

By leveraging the power of machine learning, Data Scientists can extract valuable insights and predictions from large and complex datasets.

Focus on predictive analytics

Data Science is primarily focused on predictive analytics, which involves using historical data to make predictions about future events or trends. By analyzing past patterns and trends, Data Scientists can develop models that can predict future outcomes with a certain degree of accuracy.

These predictions can be used to make informed decisions and drive business strategies.

One of the key advantages of predictive analytics is its ability to identify potential opportunities and risks. For example, in the financial industry, predictive analytics can be used to detect fraud, identify potential investment opportunities, or assess creditworthiness.

In healthcare, it can be used to predict disease outbreak patterns or identify patients at risk of certain medical conditions.

Descriptive and predictive analytics

While Data Science is primarily focused on predictive analytics, it also involves descriptive analytics. Descriptive analytics involves summarizing and visualizing data to gain a better understanding of the current state of affairs. It aims to answer questions like “What happened?”

and “Why did it happen?”. By analyzing historical data, Data Scientists can identify trends, patterns, and outliers that can provide valuable insights for decision-making.

Descriptive analytics techniques include data visualization, exploratory data analysis, and summary statistics. These techniques help Data Scientists to understand the characteristics of the data, identify outliers and anomalies, and gain initial insights before moving on to more advanced predictive modeling techniques.

Similarities Between Operations Research and Data Science

Leverage quantitative and analytical methods.

Both Operations Research and Data Science rely heavily on quantitative and analytical methods to solve complex problems. Operations Research, also known as OR, uses mathematical models and optimization techniques to find the most efficient solutions to problems in areas such as logistics, supply chain management, and resource allocation.

Data Science, on the other hand, uses statistical analysis, machine learning algorithms, and data visualization techniques to extract insights and make predictions from large datasets.

Rely on data to drive decisions

Both disciplines heavily rely on data to drive decision-making processes. Operations Research uses historical and real-time data to develop mathematical models that can help companies make informed decisions about resource allocation, production planning, and inventory management.

Similarly, Data Science relies on data mining and analysis to uncover patterns, trends, and correlations that can be used to make data-driven decisions. Whether it’s optimizing transportation routes or predicting customer behavior, both Operations Research and Data Science use data as a foundation for decision-making.

Can inform business strategy

Both Operations Research and Data Science can play a crucial role in informing business strategy. By leveraging quantitative and analytical methods, these disciplines can provide insights and recommendations that help companies optimize their operations, reduce costs, and improve efficiency.

Operations Research can help companies identify bottlenecks in their supply chain, optimize production schedules, and minimize transportation costs. Similarly, Data Science can help companies identify new market opportunities, understand customer preferences, and improve marketing campaigns.

Ultimately, both Operations Research and Data Science can contribute to the development of data-driven business strategies.

According to a study conducted by the Institute for Operations Research and the Management Sciences (INFORMS), companies that effectively utilize Operations Research and Data Science techniques can experience significant improvements in their operational efficiency and profitability.

Key Differences

Operations research emphasizes optimization, data science emphasizes prediction.

One of the key differences between operations research and data science lies in their primary focus. Operations research is primarily concerned with optimization, that is, finding the best possible solution to a given problem.

It aims to minimize costs, maximize efficiency, and improve decision-making processes. On the other hand, data science focuses more on prediction. It involves analyzing large volumes of data to identify patterns, trends, and insights that can be used to make accurate predictions about future events or outcomes.

Operations research uses mathematical models, data science uses statistical models

Another significant difference between operations research and data science is the types of models they use. Operations research relies heavily on mathematical models, which involve the use of mathematical equations, algorithms, and optimization techniques to solve complex problems.

These models are often based on deterministic assumptions and provide precise solutions. In contrast, data science primarily uses statistical models. These models are based on probability theory and statistical analysis, and they allow for uncertainty and variability in the data.

Statistical models are particularly useful when dealing with large datasets and making predictions.

Operations research is more prescriptive, data science more descriptive

Operations research is often described as a prescriptive field because it aims to provide actionable recommendations and solutions to real-world problems. It helps decision-makers optimize their processes and make informed choices. Data science, on the other hand, is more descriptive in nature.

It focuses on understanding and analyzing data to uncover insights and patterns. While it can provide valuable information for decision-making, data science does not necessarily provide explicit recommendations or solutions.

Instead, it aims to provide a deeper understanding of the data and its implications.

When to Use Each Approach

Use operations research to optimize business processes.

Operations research is a powerful approach that utilizes mathematical modeling and optimization techniques to improve decision-making and streamline business processes. It is particularly useful when dealing with complex problems that require finding the best possible solution within certain constraints.

For example, if a company wants to optimize its supply chain management, operations research can help determine the most efficient routes for transportation, minimize inventory costs, and optimize production schedules.

By using mathematical models and algorithms, operations research can provide valuable insights and recommendations for optimizing business operations.

Use data science to uncover insights and predict outcomes

Data science, on the other hand, focuses on extracting knowledge and insights from large volumes of data. It involves gathering, cleaning, and analyzing data using various statistical and machine learning techniques.

Data science can be used to uncover patterns, trends, and correlations within the data, which can then be used to make informed decisions and predictions.

For instance, in the healthcare industry, data science can be used to analyze patient data and identify risk factors for certain diseases. By analyzing large datasets, data scientists can uncover hidden patterns and develop predictive models that can help healthcare providers make more accurate diagnoses and develop personalized treatment plans.

They can complement each other

While operations research and data science are distinct approaches, they can also complement each other in many ways. Operations research can provide a solid framework for optimizing business processes, while data science can provide the necessary insights and predictions based on real-world data.

For example, operations research can help identify the optimal allocation of resources in a manufacturing plant, while data science can provide insights into demand patterns and help predict future customer behavior.

By combining the strengths of both approaches, businesses can make more informed decisions and achieve better outcomes.

While operations research and data science share some commonalities, they are distinct disciplines with different areas of focus. Operations research leverages mathematical models to optimize business operations, while data science relies more heavily on statistics and machine learning to extract insights from data.

Understanding the core differences between these two fields can help businesses determine when to apply operations research methods versus data science techniques to drive the best results. Often, combining perspectives from both disciplines leads to the most powerful outcomes.

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Guide to Discrete Mathematics pp 407–422 Cite as

Operations Research

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Operations research is a multi-disciplinary field that is concerned with the application of mathematical and analytic techniques to assist in decision-making. It employs techniques such as mathematical modelling, statistical analysis and mathematical optimization as part of its goal to achieve optimal (or near-optimal) solutions to complex decision-making problems.

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Program Description

Statistics and Analytics is the undergraduate program of the Department of Statistics & Operations Research. The BS degree in STAN provides students with a strong undergraduate background in the areas of probability, statistics, data analysis, optimization, stochastic and deterministic modeling, and risk analysis. It prepares students for careers or for further graduate study in such technical fields as statistical analysis, operations research, management science, industrial engineering, biostatistics, strategic planning, systems analysis, financial analysis, and actuarial science.

The demand for students with this type of training has been quite high in the past and should continue to be so. Students who have interests in any of these areas and who enjoy quantitative work should investigate the possibility of obtaining a major or minor in this program.

The results of our individual decisions, such as the choice of financial investments or whether to smoke cigarettes, have a major effect on our lives. On a greater scale, decisions made by major organizations have a profound impact on the lives of large numbers of people. For example, the amount of medicare benefits, flood insurance rates, the location of school district boundaries, the approval of controversial drug treatments, the location of a hardware chain’s stores, and an airline’s routes and fare schedules are all consequences of decisions made by governmental or corporate entities that affect all of us.

Although seemingly diverse in their nature, these decision problems have much in common. In each case, the possible decisions can be formulated in a quantitative manner and from them a “best” option can be chosen. Moreover, these formulations are usually quite complex, often requiring the collection and analysis of large amounts of data and the handling of elements of uncertainty. Consequently, the decision-maker must rely heavily on mathematical and statistical techniques, as well as sophisticated computer software, to formulate and analyze the problems.

Gathering and analyzing pertinent data is a crucial part of the decision-making process. In many cases, data sources are already available, for example, census data might be used in establishing school district boundaries, in others, experiments have to be designed from which to extract data, for example, medical tests might be devised to obtain reliable data on the effects of different drugs on a specific medical condition. Once the data has been collected, it must be interpreted to ascertain its implications. This gathering and assessing of data is known as statistical analysis.

Uncertainty arises, and is, in fact, pervasive, in almost all areas of the social, managerial, and physical sciences that use mathematical modeling for understanding and interpreting phenomena. In economic and financial models, the uncertainty introduced by human behavior must be taken into account, while in insurance models the analysis of risk often depends upon the random effects of nature. The concept of uncertainty is formalized quantitatively in the study of probability. The study of risk as it applies specifically to the areas of financial analysis and insurance is called actuarial science.

Decision problems are complex in that they must take into account not only all the factors that can affect the decision but also must consider the consequences of a particular decision. Since these factors rarely provide unanimous support for a specific decision, the possible decisions must be weighed against each other. Often, quantitative measures, for example, costs and benefits, can be assigned to various factors and outcomes and mathematical optimization techniques used to determine the best decision. This aspect of analysis is a major component of operations research.

The Statistics and Analytics, STAN, program, the undergraduate program of the Department of Statistics & Operations Research at the University of North Carolina, encompasses the study of these quantitative techniques relevant to the problem of decision-making, interpreted in its broadest sense. Students who choose the BS degree in Statistics and Analytics will obtain a solid background in the fundamentals of calculus, probability, operations research, and statistics, as well as proficiency in the use of relevant computer software. Those students who wish to become actuaries will find this degree program an excellent preparation for their future work and, specifically, for the exams that must be passed to become fellows in the professional actuarial societies.

Students with the BS degree in the Statistics and Analytics program typically will have a wide range of choices for employment upon graduation. The job markets for actuaries, statisticians, and operations research analysts have been quite strong in the past and are expected to remain so in the foreseeable future. In addition, students with excellent grades who desire more education in these areas will find a large number of opportunities for graduate study. Recent graduates have gone on to obtain higher degrees in Statistics, Operations Research, Biostatistics, Industrial Engineering, Psychology, Public Health, and City and Regional Planning. Combined with a few years of work experience, students with this degree will find that they are also well prepared for obtaining an MBA.

A further opportunity for students who have entered the University with advanced placement credits and who have maintained a B+ average in the STAN program is represented by the 5-year BS-MS certain graduate courses in their senior year, students can complete a Master of Science degree in Operations Research with a single additional year of study.

Operations Research

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The mission of the Operations Research Department is to provide premier graduate education in military operations research and to produce high-quality, objective, academically rigorous research and professional advice in support of military- and security-related operations.

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The NPS OR Department is one of the largest OR departments in the United States. Tenured or tenure-track faculty members hold Ph.D. degrees in operations research or a related discipline (e.g., mathematics, statistics, computer science). Most of those without Ph.D. degrees are current or former military officers who have deep military operations and applied operations research experience. 

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My OR degree from NPS has been amazingly useful in every Navy position I've held since, both at sea and ashore, and in acquisition. OR really turbo-charges your personal toolkit for real-life problem solving. And how to be a smart consumer of all the data that gets thrown at you. Invaluable!

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The Naval Postgraduate School (NPS) provided post-baccalaureate education to military officers and other members of the United States defense and national security community. The mission of NPS is to provide high-quality, relevant and unique advanced education and research programs that increase the combat effectiveness of the Naval Services, other Armed Forces of the U.S. and our partners, to enhance our national security. 

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UPC. Universitat Politècnica de Catalunya

Master's degree in Statistics and Operations Research

School of mathematics and statistics (fme).

The aim of the UPC-UB   interuniversity   master's degree in Statistics and Operations Research  ( master's degree website ) is to provide graduates with advanced knowledge of the theory and methods of current statistics and operations research. Integrated into multidisciplinary working groups, students who successfully complete this master’s degree course will be able to apply the skills acquired in areas such as healthcare, services, industry, business, science and government agencies. They will also be provided with research-focused training to help them gain access to the doctoral degree.

General details

Check the language of instruction for each subject in the course guide in the curriculum.

Information on  language use in the classroom and students’ language rights .

School of Mathematics and Statistics (FME)

Faculty of Economics and Business (UB)

The content of the degree is appropriate for graduates of bachelor's degrees that include statistics or operations research subjects. Candidates will ideally have taken a bachelor's degree and will be interested in solving problems, have an aptitude for mathematics and be skilled communicators. The academic structure of the master's degree includes homogenisation courses in the first semester and the possibility of taking specific pathways in accordance with prior learning. The aim is to promote the entry of students from different academic backgrounds. Holders of the following qualifications may be considered:  

• Bachelor’s degree in Statistics
• Bachelor’s degree in Mathematics
• Bachelor’s degree in Biology/Physics/Biotechnology
• Bachelor’s degree in Economics/Actuarial Sciences
• Bachelor’s or pre-EHEA degree in Industrial Engineering or other engineering fields
• Bachelor's degree in Informatics Engineering
• Bachelor's degree in Psychology/Sociology
• Diploma in Statistics, taking a minimum of 30 credits in the form of bridging courses.

To decide on whether students are suitable for the master’s degree in Statistics and Operations Engineering, their curriculum vitae and prior training will be considered, together with their stated interests, in order to guarantee that the aims of the Master’s Degree can be fulfilled in a reasonable time and with a reasonable degree of effort.

The elements that will be taken into account for this evaluation will be: 

• Applicants should attach a scanned copy of their curriculum vitae, an official academic certificate issued by their school of origin stating the weighted mark of their academic transcript (NPE) on a scale of 1 to 10. 
• If when pre-enrolment takes place the student has not yet finished their course of studies, the certificate should refer to courses taken and passed up to the date of issue of the certificate.
• If no certifying document is attached, the NPE will be taken to be 5.   
• Applicants should specify the academic qualification they have obtained or they expect to have obtained when enrolling.
• If this qualification has already been obtained, a scanned copy of either the certificate or the receipt for payment for this certificate should be attached to the applicant's curriculum vitae. 
• The original of the certificate or the receipt must be presented on formal enrolment in the course.   
• Aspects of the curriculum vitae related to statistics and/or operations research in the professional, teaching or scientific spheres.
• In particular, prior academic training, qualifications obtained and professional experience will be taken into account.   
• This knowledge will be accredited by attaching a scanned version of the highest level qualification or certificate obtained to the applicant's curriculum vitae.
• Without this accreditation, this item will not be taken into account when evaluating the student's application.   
• Dedication to the course of studies and whether it is to be combined with a job. 

First semester

• Advanced Statistical Inference 5
• Clinical Trials 5
• Continuous Optimisation 5
• Econometric Analysis 5
• Foundations of Statistical Inference 5
• Fundations of Bioinformatics 5
• Lifetime Data Analysis 5
• Linear and Generalized Linear Models 5
• Mathematics 5
• Models and Methods From Operations Research 5
• Optimization in Data Science 5
• Optimization in Energy Systems and Markets 5
• Risk Quantification 5
• Simulation 5
• Spatial Epidemiology 5
• Statistical Programming and Databases 5
• Statistical Software: R and SAS 5
• Statistics for Business Management 5

Second semester

• Actuarial Statistics 5
• Advanced Topics in Survival Analysis 5
• Bayesian Analysis 5
• Discrete Network Models 5
• Epidemiology 5
• Financial Statistics 5
• Genetic Epidemiology 5
• Large Scale Optimization 5
• Longitudinal Data Analysis 5
• Machine Learning 7.5
• Multivariate Data Analysis 5
• Omics Data Analysis 5
• Probability and Stochastic Processes 5
• Quantitative Finance 5
• Quantitative Marketing Techniques 5
• Simulation for Business Decision Making 5
• Social Indicators 5
• Statistical Learning 5
• Statistical Learning with Deep Artificial Neural Networks 5
• Statistical Methods in Clinical Research 5
• Statistical Methods in Epidemiology 5
• Stochastic Programming 5
• Summer School Seminar 3 3
• Time Series 5

Third semester

• Master's Thesis 30
• Compulsory ECTS
• Optional ECTS
• Project ECTS

Professional opportunities

Graduates of this master's degree will be experts who may be employed in healthcare, services, industry and business. They will apply the theory and methods of statistics and operations research in fields such as biostatistics, data engineering, marketing and finance, industrial statistics, optimisation in engineering and industry, and applications in transport engineering.

Generic competencies

Generic competencies are the skills that graduates acquire regardless of the specific course or field of study. The generic competencies established by the UPC are capacity for innovation and entrepreneurship, sustainability and social commitment, knowledge of a foreign language (preferably English), teamwork and proper use of information resources.

Basic competencies

• Graduates of this degree will have acquired the knowledge that serves as a basis or opportunity for developing and applying original ideas, often in a research context.
• They will know how to apply the knowledge acquired and their problem-solving abilities in new or unfamiliar settings within wider (or multidisciplinary) contexts related to their field of study.
• They will be able to integrate their knowledge and deal with the complexity of making judgements on the basis of information that, although incomplete or limited, includes reflection on the social and ethical responsibilities related to the application of their knowledge and judgements.
• They will be able to clearly and unambiguously communicate their conclusions—and the knowledge and reasons that support them—to specialised and non-specialised audiences.
• They will have acquired learning skills that will enable them to continue studying in a largely self-directed or autonomous manner.
• A capacity for carrying out activities that involve applying theoretical and methodological knowledge and statistical and operations research techniques using teamwork and other skills expected of graduates.
• A capacity for identifying the most appropriate statistical and operations research methods for analysing the information that is available at any given moment, in order to respond to problems and dilemmas that arise and to inform decision making.
• An awareness of the need to observe professional ethics and rules on data and statistical secrecy protection.

Specific competencies

• A capacity for designing and managing the gathering, coding, handling, storage and processing of information.
• A capacity for mastering the terminology belonging to a field in which statistical and operations research models and methods are applied to solve real problems.
• A capacity for formulating, analysing and validating models that are applicable to practical problems. A capacity for selecting the most appropriate statistical and operations research method or technique for applying models to concrete situations or problems.
• A capacity for using various inference procedures to answer questions, identifying the properties of different estimation methods and their advantages and disadvantages and adapting these methods to a concrete situation in a specific context.
• A capacity for formulating and solving real decision-making problems in various areas of application and selecting the most appropriate method and optimisation algorithm in each case.
• A capacity for choosing the most suitable software to carry out the calculations necessary to solve a problem.
• A capacity for understanding advanced statistics and operations research articles. Familiarity with research procedures for the production and transmission of new knowledge.
• A capacity for discussing the validity, scope and relevance of solutions and presenting and defending their conclusions.
• A capacity for implementing statistics and operations research algorithms.

Quality accreditation

Organisation: academic calendar and regulations, master's degree website.

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• FAQ (Frequently Asked Questions)
• UPC in the rankings

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Pre-enrolment

Pre-enrolment for this master’s degree is currently closed. Use the “Request information” form to ask for information on upcoming pre-enrolment periods.

Further information on pre-enrolment