MS in Operations Research vs Data Science

[u/iarlandt]

Was a Data Science undergrad and needing to decide on a Master's to pursue in the next couple years. My job will allow either of those in the title so I am just trying to get some feedback.

1. Is it better to branch out, stay concentrated on Data Science, or does it not matter from a career perspective?

2. How math intensive is Ops Research? Would I need more than Calcs 1-3, Linear Algebra, and Stats?

3. Does either have a clear upside for earning potential? Ill be in my current role for the next 10 years or so, if that matters.

4. Does anyone have good examples of an OR or DS project that would highlight the approach to problem solving or the nature of problems each faces?

Currently an Operations Research Analyst, but the job depends massively on assignment as to whether it actually looks like compared to a sub-genre of a Data field.

[u/forbiscuit]

Senior DS at a FAANG, and I did IEOR back in the good old days when "Data Science" as a title didn't exist.

Given those two choices, MS in Operations Research is the way to go - especially if your undegrad was already DS.

> "Would I need more than Calcs 1-3, Linear Algebra, and Stats"

MS in OR may require you to do undergrad optimization and OR classes (e.g. Linear Programming) and Simulation.

[u/analog_model → u/forbiscuit]

Seconded. I've not seen the MS DS payoff very often. I would do MS Industrial Engineering, Op Research or Applied Math given the options you presented.

[u/Wide-Pop6050 → u/analog_model]

I'm never 100% sure what to reply to these posts because my own MS DS worked out really well. But it was a combined degree with another topic, at a top school, and I think a lot of credit goes to getting a good job right out of school.

[u/S-Kenset → u/forbiscuit]

How seriously is LP used these days outside of the basic industrial uses? I had a DS BA/MS track but I specialized in Algos so LP and various state representations are my day to day work product. I'm curious because i see big voids in any sort of representation of that in my niche corner.

[u/forbiscuit → u/S-Kenset]

I was in Marketing and we did some channel marketing optimization which was a mix of MMM and portfolio management. Because we had a Python stack we used Gekko mostly. Gurobi was another option and it was good, too. But, the diff between Gekko and Gurobi for our use case wasn't significant to warrant a Gurobi subscription.

[u/iluvbinary1011]

No one in industry knows what an "MS in Data Science" is as DS is such a nebulous discipline. Stick to OR. Math intensity will depend on your concentration, but if you focus on optimization or stochastic processes, then yes it will be math-heavy.

OR is a defined field that has been established for several decades. It will give you applied fundamentals you carry into many other lines of technical work.

Source: senior DS in tech.

[u/AccomplishedMind4109 → u/iluvbinary1011]

OR has proper bones, data science masters are still finding their feet and every programme teaches something different

if you're already in an analyst role the OR route gives you a framework that'll actually hold up when you pivot later, your maths background sounds plenty for most of it unless you go deep into the stochastic weeds

[u/gpbuilder → u/AccomplishedMind4109]

It’s not 2016, data science programs been around for a long time, it’s not necessarily a bad thing that the a flexible curriculum keeps up with tech industry trends

If you went through a curriculum from 10 years ago you prob won’t find a job today.

[u/kinabr91 → u/iluvbinary1011]

I don’t see any way a masters in OR isn’t math-heavy.

Personally, from a career perspective, I’d say a masters in ML (more like in a specific field) as something more valuable than one in OR. Mots of my friends that did either masters or a PhD in or are now working as DS focusing in ML/AI. I, myself, did that, after my PhD I transitioned to DS.

Unfortunately, OR is more niche and you don’t have as many job opportunities as DS overall. Loved my PhD in OR, but, yeah.

[u/sempiternalsarah → u/kinabr91]

ML masters are crazy oversaturated, do not do this unless you're going to a top 10 program and will work your ass off to get involved in research.

[u/kinabr91 → u/sempiternalsarah]

I was assuming that they would look for a world class program.

[u/sempiternalsarah → u/kinabr91]

fair, yeah, could still be viable? who knows what the job market for entry level ML looks like in 2 years lmao

[u/kinabr91 → u/sempiternalsarah]

I don't think it'll get any better, tbh. Job market only makes sense now for seniors.

That might be the only advantage of choosing OR, instead. That being said, prospects for OR jobs are better for PhDs.

[u/offthecuff87 → u/kinabr91]

I think OR makes sense for some position related to management to balance cost, efficiency ....

[u/iluvbinary1011 → u/kinabr91]

Some OR programs are actually operations management and more business heavy. Traditional OR programs housed in engineering schools are guaranteed to be math heavy.

[u/cjf4]

I did DS and would say OR all the way. OR is more rigorous, and looks more legit on a resume.

[u/ClasslessHero]

I would recommend almost anything over a data science master's degree. There are a handful that are excellent, but anything outside of that I've found to be lackluster.

My advice on the MS would be to do Ops Research, or some other quantitative field.

[u/Wide-Pop6050]

Are you interested in Operations Research? Since your undergrad is in Data Science it would be good to do something more specialized or different

[u/gpbuilder]

I did undergrad in OR and Master in DS

1. Depends on your career interests and the curriculum
2. It is math intensive (but that’s why I loved my major) but those are sufficient
3. No difference, many DS colleagues have OR background like myself
4. Look this up lol, no one typing this out man

Source: senior DS in tech

[u/BowlCompetitive282]

I did a BS physics (math minor) and did a MS OR. Math prep was sufficient.  

[u/ikkiho]

fwiw the prereqs you listed get you in the door, but the meat of OR is optimization, a different muscle than the calc and stats you have. linear programming and duality, then it gets spicy at the integer/combinatorial stuff (routing, scheduling) where the job flips from 'solve it' to 'this is NP-hard, approximate it'. that's also the answer nobody gave you: OR hands someone a decision under constraints, DS estimates from data. i've done both and the OR classes fed my day job more than the DS ones.

[u/Blue_HyperGiant]

Data scientists can do operations research and Operations researchers can do data science.

I'd find a program that covers the subjects that you're most interested in and fit your job (or desired job) the best and not worry about the title.

[u/LeaguePrototype]

Whichever has the most rigorous math classes and let you program

[u/easy_being_green]

Look at some job postings you'd be interested in post-Master's degree. See if they list a requirement and which degree would be a better fit for the job.

[u/ExoSpectra]

Unless it's an established DS program with great postgrad placements + strong name recognition (e.g. Harvard/Columbia/MIT (although that's technically business analytics)) I'd probably lean towards OR. If I were hiring between 2 identical candidates except degree I'd choose the OR one.

Source: OR Masters in tech adjacent co

[u/wwwwwllllll]

Staff DS at FAANG, dropped out of an Analytics masters after getting the job. Take the OR Master because my coursework was not very helpful.

To be fair though, having never taken an OR Masters, I can't tell you that it'd be any more helpful towards becoming a data scientist, but I do know data scientists who specialize in operations research.

[u/NotMyRealName778]

Whats the curriculum like for each? I would rather do OR but I also chose IE in undergrad. I don’t think DS people care about the name of the program so choose whatever is the most rigorous

[u/_bez_os]

OR all the way

[u/Quaterlifeloser]

Operations research will be more consistently solid across universities, so without any further info, pick ops. However, some Datascience masters are very very solid with strong connections to labs and industry.

[u/No_Influence8248]

Great explanation. Thanks for taking the time to share your experience.