Shostakovich

Personally I'd take the Calculus series at a community college (and also the lower division linear algebra) since those are normally huge classes with similar education anywhere. But if money isn't an issue, I would take all of the upper division courses (Real Analysis, Linear Algebra, Stats/Prob Theory, other electives) at UCLA or UCI since you could meet professors who could write you good recommendation letters. I'm familiar with programs at both schools and they have multiple profs who are quite active (and well known) in the Stats/Biostats discipline. Not sure about other UC's or CSU's.

Every admissions season, many students applying to statistics and biostatistics programs are intimidated by the task of writing personal or research statements. Indeed, there is an entire sub-forum on GC dedicated to SoPs (Statements of Purpose) where there is much hand-wringing over how to craft the perfect text.   But while I can't speak for disciplines outside of the statistical sciences, I can confidently say that in stat and biostat, the evidence strongly suggests that personal statements have little impact on admissions.   I've written several posts about this in the past; here's a summary of why you should stop worrying so much about a 1-2 page essay:   1. Mathematical ability is best assessed through academic records and test scores (and to a lesser extent, letters), so it is generally quite easy to order students on this important trait.The pool of students applying to statistics and biostatistics departments isn't particularly deep, so that a major concern of even excellent departments is whether applicants can handle the requisite mathematical coursework and exams.

2. Very, very few applicants have meaningful statistical research experience before starting graduate school. As a result, many students end up working on dissertations in areas entirely different than they were initially interested in... and this is totally OK!

3. Funding in most (but not all) U.S. stat/biostat programs is allocated at the department level to the strongest incoming students, so applicants aren't typically "matched" to potential advisors who agree to fund them*. Rather, the department projects the total number of positions available and then tries to recruit up to that number of students. Once the students are on campus, they are then either assigned to a position or (ideally) have some choices available to them.   Given points 2 and 3, declarations in the personal statement such as "I am very interested in studying [X] with Professors [u,V,W]" usually carry little weight. They typically translate to: "[X] is a hot topic which I know very little about but sounds interesting, and I see on your website that Professors [u,V,W] list [X] as a research area." Which, again, is JUST FINE, since that's essentially all most people can credibly write.

4. Research potential *is* important, but the best source of information on this trait is letters of recommendation, not a one-page essay. In some fields, part of showing research potential is demonstrating that you have already thought of a reasonable project that will turn into a dissertation. Since (virtually) no one applying to stat/biostat has a "shovel-ready" dissertation idea, research potential is generally assessed using some combination of mathematical ability, creativity, and perhaps some exposure to lower-level research, all of which are best evaluated using other parts of the application.   I don't mean to denigrate the personal statement too much. There are a few key things to avoid (eg. rampant grammatical errors, aimless rambling, saying you have no intention of pursuing an academic career if you are applying to a PhD program) and of course there will be exceptions to every rule, but in general, as long as the PS is competent it probably won't affect your chances of admission significantly.   

Personally I'd take the Calculus series at a community college (and also the lower division linear algebra) since those are normally huge classes with similar education anywhere. But if money isn't an issue, I would take all of the upper division courses (Real Analysis, Linear Algebra, Stats/Prob Theory, other electives) at UCLA or UCI since you could meet professors who could write you good recommendation letters. I'm familiar with programs at both schools and they have multiple profs who are quite active (and well known) in the Stats/Biostats discipline. Not sure about other UC's or CSU's.

I think it might be a good idea to focus on math courses even at UCLA and UCI, and make sure that you have multiple courses in Linear Alg and Real Analysis completed before you go for other courses. One other thing to remember might be that you only really need three good letters of recommendation, so it could be advantageous to take multiple courses with one prof (and build a good relationship with them).
 
UCI has a small Stats department in their Informatics/CS department (although with some faculty quite active in Stats/Biostats research) and taking their Stats 120 series (upper div intro probability/stat theory) could be a great way to meet some of them. The problem might be that they don't offer too many upper div Stats courses and you'd have to either go up to the the graduate courses or take related courses in the Math department. From what I understand, UCLA Stat is way bigger and even offer a Statistics major so you'd probably have a more diverse experience there.
 
Good luck!

Personally I'd take the Calculus series at a community college (and also the lower division linear algebra) since those are normally huge classes with similar education anywhere. But if money isn't an issue, I would take all of the upper division courses (Real Analysis, Linear Algebra, Stats/Prob Theory, other electives) at UCLA or UCI since you could meet professors who could write you good recommendation letters. I'm familiar with programs at both schools and they have multiple profs who are quite active (and well known) in the Stats/Biostats discipline. Not sure about other UC's or CSU's.

Undergraduate Institution: Top 50 Public
Major: Mathematics
GPA: 3.74 Cumulative, 3.81 Major (approximate)
GRE: Verbal 161, Quant 170, AW 4.5
Citizenship: Permanent Resident Asian
Graduate Institution: N/A
Important Classes: Just the standard pure math schedule with a decent number of applied electives.
Research Experience: Next to none
Publications: None
Grants: None
Teaching experience: None
LORs: I'd guess that these were above average for a student coming out of a huge public school without much relevant research experience. One of the recommenders is pretty well known in the Stats/Biostats field and said he wrote me a "good" recommendation, and the other two aren't as well known but wrote me "good" or "great" recommendations.
Additional Information: My time to degree was only about 2.5 years, but really less than that since I was only one class short of graduating after my 2nd year. I also have a gap of about 4 years after undergrad in which I worked in some not-too-related fields.

Applied:

Biostats PhD: UWashington, Johns Hopkins, UNC Chapel Hill, UMichigan, UMinnesota, UCLA, Yale, UPenn, Brown, Emory, UFlorida, UPittsburgh, USC
Stats PhD: UWisconsin
Biostats MS: Harvard, Berkeley, Columbia, Duke, WashU, Stanford (MS Stats)

Accepted: UWashington, UNC Chapel Hill, UMichigan (MS/PhD), UMinnesota, UCLA, USC (MS/PhD), Harvard (MS), Columbia (MS), Duke (MS), WashU(MS)
Rejected: Johns Hopkins, Yale, UPenn, Brown, Emory, UFlorida, UPittsburgh, UWisconsin, Stanford, Berkeley (Brown and Emory sent me MS offers)
Attending: UWashington

Comments: I do find it weird I didn't get into a lot of my safeties while I got into a decent number of programs where I thought I was a reach/reasonable. It might be because of the respective department sizes and while some big departments were willing to take a chance on me, other smaller departments filtered me out because of things like lack of research experience and gap in academic education. I probably should have applied to less schools, but really had no idea what my chances were going in. My recommendation for future applicants who aren't sure and feel a bit shaky on their credentials is to not apply to too many smaller departments, and find a right balance of reach/reasonable/safety schools to apply to.

Most of the PhD offers were fully funded, and most of the MS offers were partially funded.

Just to be clear, I never said that 6 figures was a stone cold lock for any biostat PhD graduate, only that it seems like roughly the average for our graduates entering private industry (mostly big pharma or other medical research companies).  I'm surprised to hear that in your area MS students are earning more than PhDs, because that doesn't appear to be the case in our neck of the woods. Do you think it's a function of your being in a region which is flush with PhD statisticians? Why would companies pay more for Masters statisticians if PhD statisticians were available?
 
 
Salaries for new assistant profs in academic biostat departments are much higher than $70k; indeed, the latest figures from the  AmStat News biostatistics salary survey (see below) shows that the median starting salary for a newly hired faculty member is... $102,000. Presumably these numbers are being driven upwards by salary pressure from private industry. 
 

Just got an email from Yale Public Health that their decisions will be delayed until March due to blizzard conditions in the area.

I'm applying to Biostatistics PhD programs and already started receiving some acceptances. The Biostats crew seems to hang out in the Math/Stats section of this forum though. Good luck guys.

Long time ago I thought about doing this and did some research, even went in to discuss this with a counselor at my institution. What I concluded is that it would be better to go for a masters since the programs are very catering to your background, i.e. most of them would give you something like a year to catch up with masters level CS work. True masters programs are generally not funded, but the same is true for second bachelors where you can only take out limited loans and can't receive grants. Plus, I would think having a masters in CS is a better qualification than having a bachelors, whether you are applying for jobs or applying to PhD programs.

Good luck!

I think Shostakovich has the rankings more or less right.

I see four tiers of deparments, with the general characteristics described below:

Tier 1: Harvard, Hopkins, UW
- Most faculty members are renowned researchers
- Good graduates are competitive for faculty positions at Tier 1-3 departments.

Tier 2: Michigan, Minnesota, UNC
- Some faculty members are renowned researchers
- Very good/excellent graduates are competitive for faculty positions at Tier 1-3 departments.

Tier 3: Berkeley, Wisconsin, Columbia, UCLA, Penn, Brown, Emory
- A few faculty members are renowned researchers
- Outstanding graduates are competitive for faculty positions at Tier 2-3 departments.

Tier 4: Everywhere else
- No/very few faculty members are renowned researchers.
- Graduates unlikely to be seriously considered for faculty positions at Tier 1-3 departments, excellent students may be competitive for faculty positions at other Tier 4 departments.

Of course, this is a very rough classification, and ignores the relative strengths/weaknesses of departments in various sub-areas. It also doesn't take into account the student experience at each place -- size/strength of student cohort, course/exam requirements, average time to graduation, availability of supervisors, funding package, etc. But I think it's a helpful starting point.

I would say pretty high for most of the schools on your list. Harvard, UW, and Hopkins might be hard since their applicant pools are so strong, but all others should be good chances (with Michigan, Minnesota, UNC, Wisconsin, Penn, Brown being sort of like the next guys on the list in terms of ranking).

Your best bet is probably to apply as soon as possible, looks like some of the first deadlines have already passed.