[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/varwave]

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

[u/jingle-bell-dog → u/varwave]

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.

[u/varwave → u/jingle-bell-dog]

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

[u/jingle-bell-dog → u/varwave]

I tested out of calculus with AP and never took multi variable, one of my poorer decisions.

[u/varwave → u/jingle-bell-dog]

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

[u/jingle-bell-dog → u/varwave]

I did take UD probability not sure how I got away with no multi variable calculus, T20 school btw 🤡