I'm still amused/saddened by how little theoretical work you need to do at Stanford to get a statistics BS (they call it Mathematical & Computational Science). I get it: you don't need a rigorous foundation of math to be useful as a workaday "data scientist". That said, university is probably the last time you get to indulge/immerse yourself in deep, rigorous, mathematical thinking. Not requiring students to take theoretical courses is such a pity.
In hindsight, I am glad that I studied math at Stanford (also I ended up doing something completely different: marketing). It pushed me to think deeply and patiently about a problem at length and taught me how to mix intuition with analytic rigor.
I believe it was Paul Graham who said that math was one of the better subjects to pursue in school because it's one of the most difficult. I kind of agree with pg on this one: I was nowhere near the top of my math classes, but whenever I took theoretical CS or stats classes, I found them shockingly easy and did well with much less effort.
As a Stanford EE PhD grad, I always found the ordering between Stanford Math PhDs and the rest of us to be total, with the possible exception of some CS theorists.
I did find theory CS courses 'easy' as well, but I'm sure you'd consider straightforward some courses such as MATH230X that would be challenging for non-math majors.
I'm currently doing my undergrad in math at a Canadian University, I'm interested in pursuing graduate school at Stanford in the future and would love to talk to you more about your experience and what they would expect, let me know if there's anyway I could contact you.
Not him but in general you need tier 1 publications, recommendation letters, a strong statement of purpose (very clear idea of what you want to do research in) and faculty fit. GRE scores are generally used as a filter. They won't get you in, but they will keep you out.
Forgive my ignorance, but how many undergrads have actually published a paper? I'm not saying it's impossible, but I was under the impression that this is something that happens once you get in to grad school, not before.
I'd say a more pertinent stat would be "how many undergrads who want to pursue a PhD have published a paper". At least at my alma mater, there were many opportunities to partake in independent research during our Junior and Senior years working closely with a professor to work on an eventual paper.
More than you might think, especially at tier 1 universities with funded undergraduate research opportunities[1]. You don't have to be first author to be published.
How helpful is just being named on the paper? I've been working on a project for a year that just had its submission, not sure if I should try for good grad schools though or just target industry (Big4+Unicorn) instead.
I'm really interested in ML, so I'd like to try my hand at an MS at minimum - getting into an ML team should be pretty difficult without specific domain knowledge. If I don't like it, I'd just go into industry directly afterward.
I am taking the simplistic view, and looking at the numbers from very simplistic view. Depending on what you expect out of Phd, advancing your knowledge might mean worlds to someone, but you could learn much more practical experience in those 5 years. It's up to someone's opinion.
Let me rephrase and probably further restrict what i mean:
- If you want to work in big four(which was my assumption from Big4+Unicorn piece), you are better of not doing PHD in terms of progress careerwise. here is the reasoning, using some numbers i have seen around:
- Big 4 usually let's you start Level N straight out of school. If you do PHD, they let you start Level N+1. Master's doesn't change anything, you still start Level N.
- Phd takes (approximately) 5 years.
- Promotions take 1-2 years, depending on your ambitions, your manager etc. Assume 1.5 years.
- With the above numbers, instead of 5 years in PHD and start at LN+1, you could easily get to LN+3. If you are ambitious, you could get at least one promo in 1 year, which would put you in 1 year into LN+3, making you close to LN+4.
In all these 5 years, you'll also be banking stock refreshers, people would get to know you well, you could build reputation, and you would have a good chunk of money in the bank.
If you are targeting starting your own company, depending on what it is, phd might help (taking phd topic and making a company out of it is common). However, I believe, you could do much more in those 5 years, and you wouldn't have to go through emotional ride of graduate school as an extra stress.
Again, it all depends on what you want with phd. if your goal is working for FANG, 5 years of your youth is worth more than a phd.
I don't know exact percentages, but if you have top tier publications, or at the very least have relevant research experience, solid recommendation letters & faculty sponsorship then your chances of being admitted increase dramatically. Getting into top graduate schools isn't easy. GPA wise, usually your junior/senior year is the most important aspect. Good luck!
From my experience most PhD students haven't published anything before grad school.
To check whether this the case for your situation, you can browse websites of current Stanford students if your target discipline and check whether they published anything before their first year of graduate school.
Once I realized that I was not going to be that good at math (I bailed on a Ph.D program a few months before I was supposed to start), I went into quant finance to make money. I was okay at it, but not great. From there, I somehow ended up in the software world (mostly, I'd say, because I learned to program as a quant trader, certainly not through my coursework).
I ended up in marketing by chance: I am the 1st employee at the current startup and naturally evolved into that role because I am technical but not an engineer and like to write/say stuff.
Marketing, especially product marketing, is one of the most under-rated and under-recognized role in the startup world, but in many B2B businesses, it's just as important (or so I'd like to believe) as engineering and sales.
I posted it but the heavy lifting was all /u/aormiston on /r/pystats:
> I just finished scraping all the required and optional readings (textbooks mainly) from nearly every Stanford undergraduate and graduate level stats course. https://docs.google.com/spreadsheets/d/1d_MNmIGY7yzrpnStnZqz... I only did the suggested courses for the Data Science track in the MS Stats program. Feel free to do the others and leave links in the comments if you like. This isn't exhaustive (some courses were harder to find than others), so this'll likely be a living document. Feel free to send me anything I've missed or better texts for any subject that I should add to the document. Also, for free technical textbooks, I've found freecomputerbooks.com to be very helpful (despite the fact that it sounds like clickbait). I'd recommend you check that out before shelling out for any of the textbooks listed.
A class per 10-12 weeks is a reasonable pace with a full time job. I did a lot of my course work prior to enrolling in graduate school this way. There were a lot of late nights powered by chocolate chip ice cream and the light of my monitor, followed by not-very-productive days at work. Being enrolled helped - in my case through an extension program. Sometimes you need other people to explain things, and sometimes you need to have something to lose - just to keep you going.
The fact that you complete it says more about your character than the courses you did. Not everyone has the determination to _finish_ things. I applaud you.
I've been enrolled in an MS Statistics program part-time while working full-time. I'm around half-way done, and by the end it will have taken me three years total taking two classes at a time, although that includes a few extra courses beyond what the program strictly requires.
> The Department requires that the student take 45 units of work from offerings in the Department of Statistics or from authorized courses in other departments. Of these 45 units, eight statistics courses from the list of required courses must be taken for a letter grade.
Hi and welcome to Hacker News! No need to leave a comment saying thank you, an upvote is more than sufficient :)
In general, unless you have something meaningful to add to the conversation, it's best to refrain from making comments (even if you're comment is a positive cordial one such as the one you posted).
In hindsight, I am glad that I studied math at Stanford (also I ended up doing something completely different: marketing). It pushed me to think deeply and patiently about a problem at length and taught me how to mix intuition with analytic rigor.
I believe it was Paul Graham who said that math was one of the better subjects to pursue in school because it's one of the most difficult. I kind of agree with pg on this one: I was nowhere near the top of my math classes, but whenever I took theoretical CS or stats classes, I found them shockingly easy and did well with much less effort.