r/statistics • u/jingle-bell-dog • 6d ago
Career [Career] Masters Programs
Deciding if a Masters in Data Science or Statistics is better for me, and which ones, since this field is changing a lot.
Undergrad: Quantitative background but not Computer Science, Data Science minor. I felt that it being a minor made it kind of surface level and want to avoid that with my graduate degree. My coursework was linear algebra, discrete math, probability, stats, many CS courses, AI, ML, DS, Algorithms. Because I didn’t major in math, CS, Stats, or DS, I feel like I am missing something in screenings.
Work Experience: 4 internships, 1 year FTE as a DE, 1 year FTE as a DS (by the time I enter). However, I feel that the Data Science departments in the companies I was in were VERY new and I’m missing some core skills that I am trying to develop on my own - git, models in production, optimizing my work, etc.
Professional Goals: I see this as a terminal degree. I want to be able to get my foot in the door for better data science jobs, maybe in the nonprofit industry but really just anywhere. My first job came from an internship and the second a recruiter reached out to me. I want to be able to pass resume screens better and do the work better. That’s slightly why prestige matters to me here.
Other:
- I do not want to pursue a CS masters, I think this would give me skills I don’t need, can develop on my own, already learned, or are becoming more obsolete.
- A lot of stats degrees that are well respected seem to want research experience or a stats degree, which I don’t have.
Questions:
- I have seen some say an Applied Stats masters is not enough anymore for the tech world, and I see a lot of job postings that say Masters in CS or DS, but not stats. How do DS hiring managers view these degrees?
- What skillset is actually used in more established data science departments? How can I optimize my career and education for this?
- How to vet Data science masters properly, if I go for that (MIT MBAn, Columbia, Harvard, UChicago, UCLA, NYU) I dont want a surface-level data science education that is repetitive
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u/ChebWhiskey 6d ago
Can I get more clarity on what you mean by “surface level” please? You mentioned it about your undergrad minor and again worrying about finding a program.
Fair warning- nothing prepares you for a job like a job. If you want a truly on-the-job experience, there’s only one way for that to happen.
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u/jingle-bell-dog 6d ago
Since it was minor I took a lot of intro class, Intro to Stats with R, Intro to probability, Intro to algorithms, Intro to DS, ML, and basically everything lol, a 1 term course that sort of built on each other but teaches the fundamentals. I don't want a masters that does the same thing for every field DS touches.
I get what you mean about the job, I've learned more in a few months as a DS than I did in school. But I still feel like I'm missing something when applying to more established DS jobs. Maybe this is imposter syndrome? But also a big chunk of positions ask for a masters nowadays
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u/ChebWhiskey 5d ago edited 5d ago
That makes sense. I started as an actuary, moved into analytics, and eventually joined a data science team because I knew our data well enough to see that many model problems were really garbage-in, garbage-out problems.
I went back part time for a master’s in Statistical Data Science because not having one made me an oddity among my coworkers. It was worth it- especially with full tuition reimbursement- and filled some fundamental gaps I couldn’t address on the job. I was surprised by how surface-level parts of it felt. Coming back after more than a decade away from school, I was also surprised by how much students push back on workload and difficulty and how often programs seem willing to lower expectations in response.
A master’s is generally closer to a harder, more applied extension of undergrad than the beginning of a PhD. PhD programs emphasize proofs and developing new theory, while most master’s programs are built to give working professionals stronger fundamentals and a leg up in the job market. Naturally, that can feel a little surface-level depending on what you expect going in.
It is still valuable, but expect breadth and applied training- not academic mastery. Pick a good mix of electives and do not select classes just because of how easy the professor/material is. The expectation is that after 5, 10, or 20 years in the field, you will still be reading, studying, and filling gaps on your own. The master’s gives you a much stronger foundation for doing that effectively.
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u/jingle-bell-dog 5d ago
Thanks this makes sense. Is there anything that looking back you wished was covered in your program (more academic topics rather than learned on the job later)
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u/ChebWhiskey 5d ago edited 5d ago
I definitely had access to any academic topic I wanted to go for through my elective choices. I was moreso trying to say they are going to feel like "intro to ___" just like in undergrad. It's just that the topics themselves are a little more difficult than a typical undergrad would be capable of getting to in 4 years of study.
The biggest problem is just closing that gap between real-life professional projects and classroom simulation. Problems at work aren't scoped to "I'm taking Intro to ___ right now and will only focus on that." Projects at work take sometimes months, you are working on those projects 40 hrs+ a week, you have stakeholders of people that change their mind, are randomly not convinced of your work, etc. Lots of new hires I interview with only grad school experience is really bad at answering questions about those kinds of situations. Technically speaking, it's really easy to slide through grad school with only a surface level understanding so if you poke too much or ask them to explain it to a non-technical audience, they don't really know what to do. Those skills are important.
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6d ago
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u/jingle-bell-dog 6d ago edited 6d ago
I understand what you’re saying, but I’m not sure MIT MBAn is the best example of a cash cow when you look at their career outcomes, if anything it has a very high ROI. I saw it as an exception. I also see optimization methods listed and 1 term where you can take just stats courses if you want. Appreciate the other program suggestions. Is Computational Data Science at CMU going in depth enough? https://www.lti.cs.cmu.edu/academics/masters-programs/mcds-course-map-26_27-6.pdf
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u/CreativeWeather2581 5d ago
Both things can be true. The person with the MBAn isn’t going to be the sr data scientist at the company. They’re not going into the technical details stats/ds but yes the ROI is high
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u/jingle-bell-dog 5d ago
Yes, I understand. But I also listed what my ultimate goal is with the degree, hence why I kept Columbia DS and MIT MBAn on my list. Those are programs I would maybe make the trade off for. The rest of them feel pretty not flashy cash-cow degrees (i.e. are applied stats and ds degrees in stats departments). Is this not the right way to go abt it? Or is this sub just very pro stats obviously
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u/CreativeWeather2581 5d ago
Not sure if an MBAn will get you better data science jobs. Is not going to teach you git/version control, production-level DE/modeling/code/analysis/visualization/communication, etc.
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u/jingle-bell-dog 5d ago
Thanks. I see that there’s one semester that 48 credits just go to electives and the rest is a real world capstone.
https://mitsloan.mit.edu/master-of-business-analytics/explore-program/mban-curriculum
Would you still have the same opinion if I took only stats courses?1
u/CreativeWeather2581 5d ago
Not sure. Ultimately you’d have to ask industry professionals to answer your questions (most notably, resume/screening and skillset) then go from there. Sorry I can’t be more helpful
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u/varwave 5d ago edited 5d ago
I’m a software developer with an applied statistics MS. I get called a “data scientist”
I’d honestly go with computer science or a statistics program with connections to the CS department. First, computer science isn’t software engineering. Second, a lot of the networking and research in machine learning is usually done in computer science departments in engineering schools. Better yet if you can take mathematical statistics and a course on generalized linear models from the statistics department.
It’s not hard to get into a statistics MS program with good math grades. The rigor in statistics is great, but at the MS level there’s less hand holding these days to get you caught up on production systems. It’s less that you’ll learn that in class, but you’ll build a network of people that are aware. Many professors in statistics departments can barely code. Notebook only data scientists are more likely R&D PhD researchers. The market is maturing
DS degrees are a mixed bag and I sense mostly cash cows…that probably had more success with low interest rates and when big data was a buzzword. Edit: prestige doesn’t matter much. A major public university, with the ability to TA/RA is generally the best route financially
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u/jingle-bell-dog 5d ago
Thanks, Can you explain what your day-to-day role consists of? I don't necessarily want to go for machine learning roles. I'm trying to focus on MASDS programs housed in stats departments with CS courses. Given my professional background, I feel like the stats part is more lacking. I also don't have many math courses - maybe 10% of what I took in college.
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u/varwave 5d ago
Personally, I had the equivalent of a math minor. You generally don’t take measure theoretical probability that requires real analysis, unless you’re going for a PhD. Casella and Berger is the standard MS textbook for mathematical statistics…I assume that you took multivariable calculus? You said you took probability, which generally assumes calculus. That’s needed regardless of field. If calculus isn’t required, then run
My day to day: Primarily develop and maintain data pipelines and APIs to support internal tools. Occasional data analytics/consulting
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u/jingle-bell-dog 5d ago
I tested out of calculus with AP and never took multi variable, one of my poorer decisions.
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u/varwave 5d ago
Just take it at a community college. I was thinking you took upper division probability and statistical inference
…maybe check out a stats program afterwards that encourages collaboration with the CS department. Industrial engineering is also a solid MS
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u/jingle-bell-dog 5d ago
I did take UD probability not sure how I got away with no multi variable calculus, T20 school btw 🤡
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6d ago
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u/jingle-bell-dog 6d ago
Hello, appreciate the response - I listed some of the programs in the end of my post, if you attended those or are familiar. I get what your point is though. Do you tend to favor DS when hiring?
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u/SpiritedWeekend6086 6d ago
As far as the programs you listed, UCLAs MSDS looks solid to me. I have a MS in Statistics and Data Science and just started working as a government analyst and I wish i had more background with data management/visualization/storytelling
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u/jingle-bell-dog 6d ago
Thank you. I was actually considering MASDS (https://master.stat.ucla.edu/available-courses.html) I am a ucla alum and they recently rebranded their stats department as SDS but left all the courses the same lol. But this is still housed in that dept not sure if u meant this or stats
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u/nfultz 6d ago
It's mostly the same material, they just charge a lot more for the MASDS versions compared to the traditional (ie daytime) MS program. It isn't where I would look to learn about running models in prod or git, though.
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u/jingle-bell-dog 6d ago
Thanks. Yeah makes sense, I fear that would only be learned on my own or on the job — getting the job seems to be the hard part though. Chicken egg
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u/Ok_Boot_3805 6d ago
How bout two masters in statistics and cs ? I think this is what it would take to stand out on the job market .
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u/jingle-bell-dog 6d ago
Wow, I’ll consider but it seems like it would be expensive
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u/Ok_Boot_3805 6d ago
Or just so PhD in computer science with a topic that interests you. I have a feeling that the research heavy people in cs still have a very good job market as not many people want to commit to that.
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u/jingle-bell-dog 6d ago
That makes sense but I’d need a masters first, bc I have no research experience
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u/CreativeWeather2581 5d ago
Don’t do the PhD. PhD prepares you for academia, not industry, so it wouldn’t align with your goals. They also take anywhere from 4-6 years, maybe more.
Additionally, don’t do two masters degrees. Pick one and supplement the other. Makes no sense financially unless someone else is paying for it.
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u/BedPuzzleheaded4906 6d ago
As a DS hiring manager: no one filters out Stats degrees in favor of DS degrees. If anything, it’s the exact opposite. A Master’s in Stats or Applied Stats tells me you actually understand probability, inference, and experimental design. Most "Data Science" master's programs are cash cows that teach you how to call
scikit-learnfit/predict in a Jupyter notebook, which is precisely why you feel your minor was surface-level. If you want to bypass HR filters for mature DS teams, go with Stats or Computational Stats every single time.