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Got messaged by a C3 . ai recruiter. Read that wlb is bad and that the interview process is absurdly long, but the Glassdoor reviews are 4.2 and can't find actual hours worked posted by anyone. How's the culture really? I'd be aiming for DS consulting, something more functional but with DS/ML concepts as my differentiator.
C3.ai, Inc.
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Would recommend a master's program. Bootcamps are fluffy
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Georgia Tech OMSA. 6 month online bootcamp would not give you much other than learn a few short codes to get past first interview
Price range is sub-10k, so one of the more affordable options
No, bootcamp helps slightly but most good DS roles you won't even get an interview. Better IMO try find ways to get on DS related projects at work somehow (be scrappy) even if means volunteering for sh*t role to understand ML model development and production process, to get actual DS project experience, that's what hiring managers look for. Bootcamps are not taken seriously, in most cases.
Data Science can be very, very technical and challenging mathematically, particularly if you wish to focus on high-impact projects at prestigious firms. I have a math background and work in DS but am going to get a PhD in the fall. Not necessary, a masters would do, but it does maximize your freedom and job flexibility later in life
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You can get in with a bootcamp, but you won't be a top performer for a long time and getting interviews will be ridiculously unlikely.
Definitely masters, and be ready to do a second masters worth of independent study. Which masters to actually do depends on what you think would be harder to self-study later, and the kind of work you want to end up doing. I recommend MSBA for a generalist on the ops side, and MSCS for product development. MSCS has prerequisites you don't meet if you haven't taken undergrad CS classes.
Agree with master’s degree. Not hard, per say, just requires a lot of effort, logical reasoning, patience, and general curiosity.
I use python and SQL a lot in my day-to-day. Would recommend looking up job descriptions to see what tools and skills are needed in your industry of interest.
I learned most of my DS skills during my masters in economics (basically took all the quant courses). That said, I think the term DS is too vast and lacks proper definition. These days what used to be called decision science 10 years back gets called DS. And writing NLP algorithms also gets called DS. I think you need to be more specific around what sort of DS you want to do, and if that’s achievable or not without a masters.
Did full time and did in India from one of the top schools for Econ there. Not sure you want to go to India for your degree unless that’s where you already are though. However, I definitely think it’s possible to do similar around the world coz Economics, if you design your optionals the right way, can be more quantitative than some branches of engineering, and more stats based than most of Physics. Also more business focused than any other quant degree which I believe gives major advantages while working in DS. Definitely a degree I recommend for DS!
I’m I being naive Are boot camp really just fluff, link below really have me sold https://www.springboard.com/workshops/data-science-career-track/
I went from a BS in ME to data scientist roles (and still trying to get to a ML aligned role I would like to be in). What I did:
1. Take MOOCs in analytics courses to gain knowledge and looked for more DS or analyst roles.
2. Started masters in CS. I was able to get my work to pay for a lot of it and had to work to get my prerequisites.
DM me if you would like to chat through anything regarding how to transition.
From my research, some schools make it conditional on taking prerequisite courses at the same school, some more selective schools will require the background and won’t do conditional admission.
Fortunately mine allowed conditional admission and I took two prerequisite courses at the university before continuing to the rest of the coursework. Note that I had the math prerequisites covered through my undergrad degree.