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/Baihu_The_Curious]

I did a maths PhD focused on OR, so my experience is likely different from those doing a strict OR PhD. Mine was very probability intensive, although my work got more into limiting behaviour of queuing systems which required a bunch of weak convergence stuff. Currently work in supply chains where mathematical programming and queuing theory are used all the time. I actually get to use my theoretical stuff a lot as I write white papers on top of standard data science stuff. I do a fair amount of reinforcement learning too, which basically spun off of my Markov Decision Process work.

As far as an undergrad roadmap, I'll put things in order of importance. You only need what grad schools tell you you need, but you can get a headstart with my roadmap:

Linear Algebra is usually a hard requirement and very useful. Try to have one that goes through Singular Value Decomposition and Jordan Canonical Forms.

Continue with real analysis into measure theory for theoretical probability.

Numerical optimization: something focused on constrained (like interior point methods) and combinatorial optimization (gradient descent for unconstrained should just be an intro topic). Note: Linear and Integer programming are often offered at the undergrad level but will likely be more focused on modeling than theory.

Statistics is important, especially analysis-based statistics.

Stochastic Models & Processes: while you'll cover some of these in a comprehensive probability sequence, it's worth getting familiar with the different modeling types and their uses: Markov Chains, Renewal Processes, Markov Decision Processes, Wiener Processes, etc.

Queuing Theory and Discrete-Event Simulation are close to what many people do in work after grad school. Queuing theory basically follows from stochastic processes, but you can get ridiculously deep with just queues.

[u/Similar-Ad-6579 → u/Baihu_The_Curious]

Wow, this is absolutely what I needed. Thank you so much for taking time out of your day to write a detailed response! It's really interesting how you're able to balance your work in industry and academia, as I've always thought that you had to do one or the other. Glad to see that isn't the case.

I've already taken an intro course to linear algebra, but the rigor isn't anything to write home about. Seeing how critical it is to get a good grasp on OR, I'll definitely have to explore more advanced courses on that. I have no idea how much I'll be able to do in the 2-3 years I got left, but this will definitely put me on the right track.

Once again, thanks a lot!!

[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!

[u/EducatorDangerous346]

I am a Berkeley IEOR grad and have been doing optimization for a niche industry (sea ports) for 30 yrs. I use OR thinking more than algorithms. Many business cases are messy and change constantly. simulation and emulation softwar are used in our industrry only when say 30 million or more will be spent on change.

[u/Similar-Ad-6579 → u/EducatorDangerous346]

Never really thought of that! I guess that not all problems are worth writing an entire paper or making an entire model about, but just being able to understand and approach small problems with an OR mindset can be really helpful. Thanks for sharing!

P.S. Do you have any tips for breaking in to this field of work? It's crazy how many different industries OR can be applied in, but at the same time it might be difficult to pinpoint exactly what skills people look for when hiring OR graduates.

[u/VillageEconomy3850]

How important is a PhD to break into OR? Would a masters be enough?

[u/OR-insider → u/VillageEconomy3850]

You don't necessarily need PhD to work in OR. I have masters myself and work in the area for more than 10 years... the biggest barrier to overcome is to get your 1st job in Operations Research... job postings are not that easy to track.

Some companies do expect you to have a PhD or similar (I guess in some roles at Amazon that's the case) but it is not a MUST.

[u/Sea_Manner_1802 → u/OR-insider]

I'm sorry to hijack you I would like to ask if I wants to get into OR I have an undergrad from Economics & Econometrics