An Operations Research (OR) analyst turns messy business problems, like how many trucks to send where or how to schedule nurses, into math models that find the best option, then convinces managers to act on the answer. Most people in the role have a bachelor's or master's degree, so this roadmap builds the math, coding, modeling and communication skills in three stages, all with free resources. Each skill rests on the one before it: you learn data and statistics first, then optimization, then simulation and real-world delivery. You can do this by building small projects you can show to employe
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Every stage from beginner to expert, with lessons, practice drills, projects, interview questions and free resources. Free with a Rolearch account.
Stage 1 of 3 · about 14 weeks
Get comfortable with the math and Python you need, and learn to describe a business problem as data, variables and a goal.
Linear algebra and calculus essentials
Every optimization model is built on vectors, matrices and derivatives. If you understand why a constraint is a line or plane, solver output stops being magic.
Probability and statistics
Real inputs are uncertain: demand varies, machines fail. Distributions, confidence intervals and hypothesis tests let you validate data and say how sure you are.
Python for data work
Python is the everyday tool for cleaning data, running solvers and building simulations. Writing code means your analysis can be repeated and checked.
SQL and data gathering
Most company data sits in databases. You must pull and validate your own data, which is a core part of the job.
Problem framing
Managers rarely ask for a model. They say 'our deliveries are late.' Turning that into decision variables, objective and constraints is the heart of OR.
Then 2 more stages (about 52 weeks in all) with lessons, drills, projects, 6 interview questions with strong answers and free resources.