wine in coffee cups
-
Posts
242 -
Joined
-
Last visited
-
Days Won
1
Posts posted by wine in coffee cups
-
-
Best case scenario: "we wanted to welcome you in person to the department."
Middle case scenario: "we are interested in you, but have some concerns about your application and wanted to talk to you more." (aka interview)
Worst case scenario: "unfortunately we can't make you an offer this year, but you were rejected because of ________ and you can reapply more successfully next fall if you address this."
-
You'll have a hard time finding anything paid that is described as an "internship" and appropriate for you because you are between degree programs. Usually paid internships are at larger organizations with formal hiring/career pipelines and are intended for upper-level college students who might come back full-time in the next year or two, or for mid/later stage PhD students hoping to enter industry (again in the next couple of years). I think the big tech companies like Google/Microsoft/etc. hire research interns in earlier stages of the PhD sometimes, but obviously those positions are very competitive.
I don't think the kinds of opportunities you're looking for are going to be found via search engines, it'll have to be more like making your own position. You definitely need to be networking. You might see if any alumni of your college at nearby companies/organizations would have need of short-term contracting help with data analysis in the summer months. Also, university departments and offices sometimes have use for these kinds of short-term things, ask around your current institution. As an undergrad, I had a great experience doing data analysis for a dean's office one summer when my college was preparing to undergo reaccreditation and needed a lot of assistance with reports.
Since $$$ is your immediate need, something completely unrelated to statistics would be fine too, like working at a summer camp. It's your last opportunity to spend your summer not in front of a computer all day, after all.
-
I think it depends on what exactly you mean by "industry". Obviously not having a PhD closes doors on some more research-focused positions, but hard to say beyond that without more specifics. Many positions at companies with "analyst" in the job title would be fair game for a stat master's, though the required competency with databases/programming and specialized industry knowledge will vary quite a bit.
A place to start if you are utterly lost might be to search LinkedIn profiles for people who have a M.S. in statistics from universities of comparable quality or similar location to yours and see what they're doing now. That way you can also see which technical skills seem to be important enough to highlight and what you might work on if you prepare to exit your program. You can also look up salaries for a lot of medium-large companies or job titles on Glassdoor, which I've anecdotally found to be accurate.
-
Gmail users be warned! A friend (domestic applicant) got the Duke interview/visit notification but it went to spam. They apparently sent it a few weeks ago, but my friend didn't see it until recently.
-
Hi Wine,
are you sure these 45 people are all admitted to the PhD program but not PhD&MS combined?
Yeah, I think the 45 admitted last year were for the PhD (though you can get your master's on the way as a PhD direct admit student). An additional 50 were admitted for the fee-based MS. The enrollment numbers on each line (16 for PhD, 17 for MS) correspond to the number of new bodies here, so I assume the admit numbers are right too. (I will grant you that both programs having the exact same admit rate in 2013 looks suspicious.)
There are also a handful of concurrent degree master's students (students getting a master's doing a PhD in other departments) and I'm not sure which bucket they went in, if any, but there's only a few of them.
-
Does anybody know how many PhD students uw stat will give admission for 2014 Fall? ...
In recent years around 30-50, you can nose around some data by department here: http://www.grad.washington.edu/about/statistics/admissions/
(ignore the Statistics: Advanced Methods and Data Analysis line in the reports for the past couple of years, that's for the fee-based master's program)
-
Campus visits are inconvenient for everyone, current students missing days of classes/exams and people working full-time alike. There's also not that many weekends for them, so they tend to collide if you get in to a bunch of places.
I would definitely prioritize. Go on at least two visits, but more than four or five is probably overkill. If you aren't sure that a school is one of your top choices, maybe wait a little bit before RSVPing for a visit to see where else you get in. If you think you're unlikely to go somewhere given your existing options, decline the offer ASAP and don't bother visiting.
Keep in mind that you might be able to string together consecutive visit days in North Carolina, the Bay Area, or the northeast and take only two days off.
If an event is scheduled for Wednesday-Saturday, there will probably be stuff happening on Thursday starting in the morning or a Friday social event at night, but Wednesday and Saturday are really just arrival/departure days. At least one of Thursday or Friday are worth your vacation days if you can spare them. I would try to go on whichever day will let you maximize meeting with faculty and having contact with current students. Things like going over courses/requirements and faculty/student research talks can be interesting but not as informative. You should ask for a tentative itinerary from the department admissions coordinator before arranging flights for one of these scheduled events.
Don't concentrate your visit on a weekend, no point because nobody will be around. Avoid spring break for the same reason.
-
Im going to look into taking these classes. Some of the programs have acceptations to this as long as you take them within the first semester.
I think you have more than a semester's worth of background to make up. You might look into post-baccalaureate programs in math first. Of Boston-area schools, I know Brandeis has one with a track for grad school preparation (a year of algebra, analysis, and electives): http://www.brandeis.edu/departments/mathematics/graduate/certificate.html If you're female, Smith College has an excellent (paid!) math post-bac: http://www.math.smith.edu/center/postbac.php
-
Looks to me like you need like two more years of undergrad courses (a year of algebra, a year of analysis, topology, other topics like complex analysis or PDEs or differential geometry). You've only got the first half of an undergrad pure math major completed and don't meet the prerequisites for most graduate level math courses at these schools.
- TenaciousTurtle and Fife
-
2
-
I'd like to attend graduate school in either statistics or data science. I don't actually want a PhD; I want an MS. However, I don't want to drop $50,000 on an MS when I can go to a PhD program for free and drop out after getting an MS. That being said, I want a degree from a top-ranked program, and I'm worried about my chances of getting into a top-ranked PhD program, but confident about my chances of getting into a top-ranked MS program. Since I want a top-program more than I want the free tuition, I'll spend the $50,000 if need be.
Just want to warn you that going to an academic statistics PhD program where you take theoretical coursework for the first couple of years is not necessarily going to be great preparation for a job. If you are pretty certain that you don't want a career in research, I would advise against this route.
Assuming you have no full-time work experience, you would probably have an easier time preparing for a solid entry level job by ponying up for an applied statistics/data science MS and taking more practical coursework than torturing yourself on the PhD track. From what I gathered from the people who left my PhD program last year, the person with the most work experience had by far the easiest go of job hunting. The others were cramming in CS electives to patch up their backgrounds because they were stumbling on technical questions on interviews, database-y things. They weren't getting asked about things they had learned in required courses, like properties of exponential families or the Neyman-Pearson lemma.
You would also have better support in job hunting coming from a more professionally-oriented program. Faculty know a lot about getting an academic job, but usually not much about the kinds of jobs you would be looking for. And burning bridges is never a great idea.
-
I strongly disagree with the recommendation for NC State.
This is a Master's degree. As such, brand matters a lot, since it seems like you will go into industry.
and I disagree with your strong disagreement about the NCSU recommendation! They appear to have a strong placement record (though unsurprisingly NC-heavy) and are completely transparent about outcomes: http://analytics.ncsu.edu/?page_id=248
-
Stanford's acceptance rate is not comparable to other departments because of the math subject GRE requirement. The acceptance rate is relatively high only because many potential applicants are filtered out by this requirement.
-
I know of at least one person who was admitted to both the stat and biostat departments in the same application cycle.
-
My SoP more or less followed the structure cyberwulf listed. It might be easiest to start out with that as an outline of topics and try to address each of those pieces discretely, perhaps as if these were questions you were answering in a job interview. Once you have these pieces written, then try to stitch them together into a document with either coherent transitions or clear section headings (e.g. "Academic preparation", "Research experience", "Professional experience", and "Goals"). I remember getting stuck on how exactly I wanted to put things, so what I ended up doing was just using the first words that come to mind and fixing things later.
Give concrete details about things that piqued your interest in courses you took and projects you worked on. Justify how those have prepared you for graduate study in statistics or why they made you more interested in it. In your case, your economics research and trader job might have given you opportunities to collect and process raw data, use programming languages, communicate and visualize technical results, account for uncertainty in forecasting, think critically about biases and unobserved data, etc. (I'm just winging it here). If you can connect things you've learned/thought about from courses or work with areas of research you might be interested in, all the better.
Hopefully after you've done this you have something a bit under 1000 words. If it's too long, see you if you are repeating yourself anywhere and ask a couple of trusted friends who write well to look over it and identify the weakest spots to omit or rewrite. This kind of feedback is really helpful--you learn that points you glossed over might actually be interesting enough to an outside reader to deserve more space, and other parts you felt were important might come off as overdone and get trimmed. When you are satisfied with the content, then you can wordsmith a bit more and then send it off for more feedback from the people who are writing your references.
-
I think your application is pretty strong. I'm sure it will be stronger if you take more math courses of course but I think everyone here overvalues having graduate level analysis, measure theory etc on your application. I think part of the reason you didn't get accept to some those programs is your lack of GRE subject test, not necessarily due to the lack of graduate math courses.
When I was visiting schools a few years ago, I'd say about 80% of the students I talked to didn't have many graduate math courses and many never had measure theory prior to entering the program. The places I visited were not low ranked either, I'm talking about accepted students at places like cornell, wisconsin and UNC.
In fact when I talked to the graduate director at UNC, he had said that I had more math background than the average accepted student and in terms of analysis I had only had 1 semester of real analysis (undergrad), and two semesters of advanced calculus (also undergrad).
I don't think Lelouch Lamperouge necessarily needs graduate level math coursework to be competitive, but rather a little more upper level undergraduate math coursework. You indicate you had two semesters of advanced calc plus one semester of real analysis, which might not sound like a lot to you, but that's still more than what LL was working with (one semester of advanced calc, and an A- at that). I think more applicants are coming from math major backgrounds than applied stat major backgrounds, too, so even if they don't have graduate coursework or measure theory, it's still common to have good grades in other upper-level courses like topology, abstract algebra, and complex analysis to vouch for their math abilities.
If you had read through their admissions sites, I know stanford and UW's programs clearly state they require the subject GRE.
Neither of UW's programs require the subject GRE. I don't think many students from the past few years in the stat department took it even when it was supposedly "strongly recommended". Perhaps expectations differ for international vs. domestic applicants, though.
Have you considered doing a Masters degree first and using that as a stepping stone to a high-ranked program? Your record is such that you are a lock for admission at virtually every Masters program. Sure, you probably won't have guaranteed funding, but perhaps your letter writers could play their contacts for a suitable RA position? If you were to rock a top MS program, then combined with your undergraduate record I think you would find your chances of admission at good places dramatically increase.
cyberwulf, I'm wondering if you might elaborate on why you think a master's is going to dramatically increase his chances? He's already shown he has great grades in undergraduate and graduate statistics coursework at a top 5 public with a good stats department, so his ability to handle the classes doesn't seem to be the concern. He self-reportedly already has good letters of recommendation from statistics faculty, too, and even some research experience. To me, it seems like he could take a measure theory class (or other advanced analysis, or graduate level stat theory) as a non-matriculated student and ace it to patch the only obvious hole in his profile for a much lower investment of time and money. Just curious what a 1-2 year master's program that's partially funded at best would meaningfully add when he appears to be already strong in the areas it could enhance.
-
Just guessing, but it sounds like your problem could have been a lack of advanced math coursework? I see advanced calc with an A- and that's it. Was the graduate probability class measure theoretic or otherwise rigorous and proof-heavy? If it was, you might mention that explicitly in your statement of purpose and ask your recommenders to do the same.
If not, maybe you should take one or two more advanced math classes like measure theory or functional analysis before you reapply and do your best to get an A? A good score on the math GRE seems like it could help in your situation, but realistically, I think that's a longshot since you haven't taken algebra, topology, number theory, etc. while almost everyone else taking the test has.
Being a non-resident didn't help you either, but I also notice from your posts that you "write with an accent". When you applied last time, did you have your essays looked over by a native English speaker before you submitted them? Most applicants are not native speakers so some degree of grammatical errors is tolerable, but just want you to make sure what you sent didn't come off as sloppy.
Most importantly, you should apply to some larger programs. Most of the departments you applied to last year are small or medium sized, and all are highly ranked or at Ivy League universities that draw many applicants just because of the school name. (Kind of looks you used a name-based strategy last time around?) The biostatistics programs you're thinking about are all very selective, too. You ought to think about sending applications to some larger statistics or biostatistics departments like NCSU, UNC, and Iowa State, which are still well regarded and offer broad research opportunities but are big enough to make offers to more than the top couple dozen applicants.
-
It looks like NSF is answering questions via Twitter this year: https://twitter.com/NSFGRFP and https://twitter.com/MullerParker
For example, this conversation clarifies that the three statements have been collapsed into two because reviewers felt there was redundancy, but their content is not strictly the same as what was asked for before:
“The two statements are different from the three essays used in previous years (not “folded in”). personal, educational &/or professional experiences that motivate your decision to pursue advanced study” is new. ....and focuses applicants on career goals & how past experiences (undergrad research, other activities) influence goals & grad school prep.
-
cyberwulf, I'm not sure I would hold up a perfect GPA as the standard for a stats applicant to be considered uber-competitive and in the peer group with aridneptune, but let's say something like >3.9 within math (with lots of courses), and not much lower overall from the kinds of schools you've heard of. Without weird weightings, that essentially means over 3/4 of math grades are A's and the remainder are A-'s, which sounds like a reasonable peer group to me. Given that, I'm quibbling with your quibble (instead of working on my research
) and think you're underestimating how many stats applicants are out there with comparable profiles.I had a job some years back analyzing data for a consortium of 30-odd highly ranked private colleges, more or less the same ones that are vastly overrepresented among baccalaureate institutions in PhD programs. One of the fun facts I learned was that at pretty much all of these schools, math majors disproportionately had very high GPAs. My undergrad is a typical example: over one-third of math majors in my year graduated in the top 5% of students (>3.9 overall). Nearly all the rest were still in the top 25% of students (>3.7 overall, almost certainly higher in major). When I used to do resume review and interviewing in my old job, too, we looked for econ or math majors with high grades in statistics/econometrics classes mostly from fancy schmancy schools like Dartmouth and Wesleyan. There, too, I encountered a surprising number of students with very strong math backgrounds and grades. 3.9s in math, apparently not rare at all and not limited to those with math or econ PhD aspirations! Not certain why, but my observation is that a lot of students won't touch non-required math unless they've already gotten a lot of A's in the lower level courses. By this selection mechanism, then students who continue taking mostly math (and related classes in CS, econ, physics, or logic) will continue to get mostly A's and A-'s, especially since upper-level classes rarely curve.
So sure, the best math majors mostly go to pure math PhDs, I wouldn't dispute that. But because of the high concentration of near-perfect grades among math majors, at most good schools there will still be at least one math-ish major (current or someone who graduated in the past few years) who meets the high grade standards in my first paragraph and is applying to statistics or biostatistics programs. In the aggregated stats applicant pool, count on at minimum a dozen strong applicants from LACs like Pomona, Swarthmore, Amherst, Carleton, Williams, a bunch more -- even little ol' St. Olaf is a powerhouse. I think I'm underestimating the LAC contingent actually. Then count on a couple dozen more applicants from the mega name-brand research universities (Stanford's programs alone must send out a half dozen people at a time and I think Chicago too). Throw in another couple dozen apps from other respected schools like UNC. Basically, name all the good private and public schools you can, and imagine that there's on average one current senior or recent grad with strong credentials applying to stats departments (because, again, high grades are not at all unusual for math majors). And surely most of the recommendations for this crowd are quite good, especially for the substantial portion of these applicants coming from small schools who get detailed personalized recs or those with access to bigger names?Anyway, a couple dozen here and there ballparks to something like 60 applicants in the pool who are going to be at the top of the piles everywhere they apply, so I think a fair number more than what you have in mind. It would be a big pain, but you could look up profiles of first-year grad students at the top 15 or so statistics departments, and I bet you would find enough students who majored in something math-ish graduating with high honors (proxy for high GPA) at a well-reputed undergrad to substantiate my claim. In my cohort alone there are at least 6 of us who would meet that description, have to imagine the scene is similar elsewhere. -
Thanks very much for the advice. My programming experience is indeed limited (basically STATA and SAS for empirical research). I meant Berkley / Washington MS programs above (excuse the confusion there). I also appreciate the SOP advice (and the pun) - do you think that the 'SOP-is-of-limited-importance-in-statistics' advice in the sticky on these forums is relevant?
Perhaps the best course of action would be to get an MS, prove my bona fides, and then move onto a PhD? I'm of mixed feelings about this. On the one hand, it's a greater financial burden and two years of opportunity cost. On the other, it's a shorter degree (I have the option to go terminal), is perhaps more marketable in the private sector and will give me time to get up to snuff. What do you guys & gals think?
nononononono go right for the PhD. You don't have to prove yourself in an MS first with your credentials. A US citizen (right?) who studied math with a pretty much perfect GPA from UNC should be competitive at top PhD programs. (I would gently point out that DMX above is an international applicant and so his/her experience might be better thought of as a lower bound on how a similarly credentialed American might do -- acceptance rates are way higher for citizens/PRs.)
I respectfully disagree with cyberwulf's advice to not worry much about the SOP in the stickied topic, at least wrt to the most selective statistics departments (like the ones you are interested in). Granted, I toooootally believe him in the context of most biostat applications, where some very good programs like UNC and UMN still accept so someone with good recs, high grades, and enough math is almost always going to be to be on the right side of their dividing line.
But relative to most biostat programs or lesser known statistics departments, the top statistics departments have a much larger volume of applicants, and I have to think that a much larger proportion of them have strong math and statistics backgrounds (as opposed to maybe more coming from a bio-related track and meeting math requirements minimally). So though I think you have a decent shot at top stats departments based on sheer numbers alone, nonetheless consider that many of the best colleges/universities in the US are going to have one or more math majors with very high GPAs applying when you are, and they're more or less each applying to a subset of the same ~20 statistics programs plus Harvard, UW, JHU biostat. That is, functionally there are several dozen aridneptune doppelgangers out there (plus other strong applicants from around the world) and a large proportion of them are competing for spots with you at each place you apply. Any program that doesn't accept a majority of such students is going to be iffy for you.
Thus I think if you want to improve your chances at your top choices, you should do whatever you can to state your case about being qualified and a good fit within each department. This goes triply extra for any smaller programs you might end up applying to with fewer areas of active research (e.g. Harvard, Yale, Northwestern). Also, remember that the SOP is the only piece of your application you have complete control of at this point: your transcript and test scores are what they are, your recommenders will say whatever they want. (Though they might be more likely to say things you want them to say and reinforce your positive characteristics if they can look at your SOP draft when they write it!) So whatever your perceived weaknesses are, you have the opportunity to frame them however you like and draw attention to your strengths. Let's also not forget that it's pretty stupid to pay $100 per school in application fees and GRE score sending and not give every piece of your application your all.
So yeah, I say do worry about the SOP, really put your back into it. It paid off for me -- I was told as much by several of the schools I got into. Try not to have your app come off too strongly as "why I don't want want to work in finance anymore". Work on conveying the "why I do want to study statistics" positive message in a personal and authentic way. Let there be no doubts that you should be in a statistics department instead of an economics department or a PhD track in a business school. Being out of school shouldn't hurt you, but you need to make them believe you belong there. For starters, you'll want to mention your self-study in statistics and write about any interesting statistics-related projects you've worked on (as a student or at your job) that led you to want to do research in statistics.
Good luck etc.
-
Like kimolas said, I think that will vary tremendously among programs and industries. In my department, I only know of one first-year student in my cohort doing this, though I think last year there were 2 or 3. The rest stay and do research or teach/TA summer courses. Some internships may also only want more advanced students (year 3-4ish) who have finished all their coursework and have research experience already. I think that is fairly common in pharma.
I do remember Berkeley making a fuss about getting CA residency after the first year for out-of-staters, but I don't think that's true of all public programs (for instance, not mine).
-
I think you'll have a hard time finding graduate students in statistics who can make use of you for research help. The main types of work grad students want to outsource are things like manual data entry and cleaning, nothing very statistical. You'll probably find more opportunities for that outside of statistics in areas like economics, psychology, and ecology.
You could look at Kaggle.com competitions to get some practice. I don't think it'll mean much admissions-wise but it will be good experience for you.
-
For analyzing data, R is the biggie. You'll probably see a fair amount of SAS if you are in biostatistics (more the traditional clinical trials side than genetics). Statisticians don't use Stata or SPSS, but collaborators/consulting clients in the social sciences use those heavily, so just passing familiarity is nice to have in those cases. C and MATLAB are more specialized -- I'm sure some people in my department use these all the time, but the majority basically never.
I don't like to do heavy data cleaning and manipulation in R, and certainly not for anything large. For general-purpose data processing, Python is useful. For any language, knowing regular expressions is very, very useful for cleaning up messy data. I definitely recommend picking up SQL for data extraction and aggegration (which you can use right in R or SAS)--some of the students who were leaving with a master's and looking for industry jobs found that they often wanted SQL skills.
For papers, reports, and presentations, you need to learn LaTeX. I used knitr in RStudio to integrate the TeX with R output and graphics seamlessly.
-
This is a very good suggestion. Download and install R and then download RStudio. Both are free. RStudio offers a clean interface to organize your code files, console output, workspace objects (you can double-click on your dataframes/matrices to look at them!), and plots. I'm really surprised it isn't more widely used.R. So much more robust, and you'll seem (to others) smarter for it. If you want a GUI, get RStudio.
RStudio is also well-integrated with Sweave/knitr so that I can do my data analysis in the same file as my write-up. Then all my tables, regression coefficients, plots, etc. end up right in my document or slides without any copying/pasting or manual typing of Word tables if I use R packages like xtable. Just click a button and it spits out a PDF. Definitely a learning curve there but saves a ton of time in doing homework or making presentations once you have the hang of it.
-
I would focus your search in Somerville somewhere around Davis and Porter, basically the region bounded by Teele Square, Winter Hill, Porter, and Union Square. Tons of Tufts and Brandeis grad students live in those neighborhoods. You'll be in walking distance from the Porter commuter rail (can maybe take the T one stop from Davis or a bus to shorten that up) and he'll be in walking/biking distance of the Tufts campus (with the option to partially cut that down on a gross day by taking a bus).Hey folks. A buddy and I will be heading to this area in the Fall, and we'd like to room together. Thing is, he got into Tufts while I got into Brandeis. It looks like we'll be on two different ends of town. Neither of us will have cars, and I'm happy to take public transit out to campus. I see that the T runs straight to Brandeis, so that's convenient.
Anywho, can you guys think of any areas that have good access to transit routes to both campuses? I think he's leaning toward Cambridge, Somerville, and Medford itself. I'm definitely leaning away from Waltham. Maybe Porter?
Statistics internships for the summer before grad school
in Mathematics and Statistics
Posted
Start reaching out to people now. Maybe you'll get told to check back in a month or two, but it can't hurt to be making connections sooner rather than later and putting your name out there.
Also, you may have already done this, but make a LinkedIn profile, fill it out thoroughly, and make as much of it public as you are comfortable with. My profile is quite sparse but because of some listed technical skills and being part of my former employer's network, I get recruiters contacting me from time to time.