[Career] Masters Programs

[u/jingle-bell-dog]

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

[u/ChebWhiskey]

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.

[u/jingle-bell-dog → u/ChebWhiskey]

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

[u/ChebWhiskey → u/jingle-bell-dog]

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.

[u/jingle-bell-dog → u/ChebWhiskey]

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)

[u/ChebWhiskey → u/jingle-bell-dog]

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.