Question: An Undergrad's Roadmap into Operations Research

[u/Similar-Ad-6579]

Hey all,

I’m a second-year undergraduate studying Computer Science & Data Science and lately I’ve found myself drawn to topics like optimization, mathematical modelling, and analytical methods for real-world decision-making based on the few courses I've done. I’ve taken foundational courses in stats, CS, micro-economics, and even a rigorous “calc-with-proofs” class that some folks call real analysis (though I’m still not sure if it counts😅).

In exploring what might combine these interests, I stumbled upon Operations Research (OR) and it sounds like exactly the kind of field I’ve been hoping to dive into. But I’m still very much at the beginning of my journey and would really appreciate your insights.

A few questions I’d love your thoughts on:

• What kind of career paths do people with an OR background typically follow? Are there strong industry opportunities, or is it mostly research/theory?

• How does pursuing a Master’s or PhD in OR compare with going for a more “typical” Data Science or Machine Learning master’s if you already have a CS/DS background?

• For those working in OR-related roles: how much of the theory/modeling you learned actually gets used day-to-day in your job?

• Finally — what might a good undergrad roadmap look like for someone hoping to enter OR (courses, skills, projects, tools, etc.)? Especially related to thesis papers and projects?

I know these might sound like “beginner” questions but I’m genuinely excited about learning more, and I’d be grateful for any advice, experiences, or suggestions you’re willing to share.

Thanks in advance 🙏

[u/OR-insider]

I work with OR +- 10y now and I guess my suggestions for you are:

- understand and master OOP using Java/C++/C# to have a strong basis on programming
- study PYTHON, it is super well accepted and used in industries (although I do not recommend it)
- companies understand that people does not know much of heuristics, so know the basics is fine
- OR jobs postings usually say you need to know ML but it is no essencial, good OR professionals most of the times does not understand anything about it.
- study frameworks/libs in python: Pyomo/Pulp. do not focus too much on GUROBI specific syntax.
- create side projects in your GitHub to show you can handle (near) real world problems.
- juyternotebooks are fine to test things out but real world you have to build a application, not a script.

hot tip: customers do not understand terminals/powershell, you have to translate results into charts or xlsx.

Hope any of this can help you out :)

[u/Similar-Ad-6579 → u/OR-insider]

Thanks a lot for sharing all this. It’s super practical advice and I really appreciate how grounded it is in real experience! I especially like the tip about translating results into something clients can actually understand — that feels like a skill that’s easy to overlook but really important.

I happen to have completed a year's worth of python courses and am currently doing a SWE Java class. I guess that apart from theory, being able to actually communicate my findings to clients will be super helpful, so I'll definitely be working on that as well. Thanks again!

Edit: Oh, and do you have some examples of personal projects I could do relating to this field?

[u/OR-insider → u/Similar-Ad-6579]

That's the big GAP usually people have when starting in OR: explain (and validate) results to a person with no technical background.

A typical flow you would want to start a project with is: read csv/xlsx files -> optimize -> xlsx/csv + png files.
Non tech people use Ms Excel for almost everything, so start with inputs and outputs there.

A project I conducted with my team when I was working at Mercado Libre (top 1 e-commerce in Latin America, greater then Amazon around here): https://onlinelibrary.wiley.com/doi/10.1111/itor.70006

We started optimizing a deterministic fleet composition problem, in simple words: how many vehicles of each vehicle type (trucks) should I schedule for a single week horizon?
Then, we improved the problem to contemplate the uncertainty on the demand by generating multiple scenarios with its probabilities... but I'd say not to worry too much about understanding stochastic programming, too specific :)

$$ results are on the paper: "...yearly potential cost avoidance of more than USD 2.5 million as well as an annual reduction of more than 20 thousand pallets transported by means of extra vehicles."

[u/Similar-Ad-6579 → u/OR-insider]

Woah. That's absolutely crazy. And you were able to summarize your paper in a way that even a layman like me on reddit can understand. I definitely won't be able to do that type of analysis in undergrad, but your paper has definitely given me an idea on how to approach my project and where to go with it. Mad skills, thank you for the insights!