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Found 107 results

  1. I'm fortunate to have been accepted to both programs. Based solely on research faculty and rankings, which is stronger? I can't find much information regarding the biostats program. Is it clumped together with the bio, med, or stats dept? In addition, if I'm looking for industry work which would look stronger? If you have any info regarding internal PhD conversion rates or on whether Duke's program has been on the up and up please let me know!!
  2. There are a few other current PhD students who frequent this forum. I've visited it on and off over the years, but I have not seen many posts from current PhD students about their experiences. I thought this may be of interest to potential applicants, so I decided to write about what I have learned (I am about to graduate, finishing my final defense and thesis in May). I am happy to report that my PhD experience was largely positive. 1) A PhD program is fundamentally a research degree, and research is nothing like taking classes. I think some Stat/Biostatistics programs do a great job of involving students in research early on through rotations with different professors or through reading courses to familiarize students with statistical literature. But there are a lot of programs where students do not start research until the end of their second year. And I have seen many PhD students who were very, very bright (acing all their classes, 4.0 GPA, etc.) but who really struggled with transitioning from being a student to becoming a researcher. I definitely think you should work hard in your classes so you can pass your written qualifying exams and so you can developed a solid foundational understanding, but once you get to the research stage of the program, you really do have to teach yourself a whole new area. Moreover, research is about discovering something new and pushing the boundary of your field. There is just no way of knowing if some "open problems" can be solved or not! It's not like solving a problem on a homework set where there is generally one correct solution/approach. If you do a theoretical topic for your dissertation, you need to prove new theorems that have never been established before, not just "show” something that already has a known solution. And even if you start working on a problem, you may get stuck for long periods of time (or need to cut your losses and give up), or you may end up somewhere completely different from where you started. Unlike problem sets and exams, there are no concrete solutions. For example, for the first paper that I wrote, I was stuck on a proof for my main theorem for three whole months. Nothing I tried seemed to work! But my PhD advisor pushed me to keep trying, and eventually I found the technique that worked. Phew! 2) A lot of the learning in grad school happens outside the classroom, and you need to ask questions. This comes from talking with your peers, meeting with your advisor, attending departmental seminars, and reading papers. Here is the thing: when most people start research, they do not yet have the skills to really excel at it. A small number of people are able to excel right from the get-go, but for most people, it takes a bit of adjustment, and that's okay! It is important to reach out for help if you need it. If I didn't understand an author's proof or a new concept that I had never encountered before, I would ask my advisor to help me. I didn't have much experience with high-performance computing or running simulations on multiprocessing systems, so I asked my more experienced classmates to help show me how to navigate it. 3) Everybody thinks about quitting at some point. This is perfectly normal. A PhD can be a very demoralizing, frustrating experience. Plus, things can happen in your personal life that can derail you. It's just part of life. When I felt like quitting, I just took some time off... maybe 2-4 days of not doing any work to recuperate and assess why I was putting myself through the PhD. After some time off (not too much time off), I could reason to myself why I wanted to get a PhD, and I got right back to work. So if this happens to you, accept your feelings, take a breather, and then really question your own motivations for pursuing a PhD. If you can answer this question to yourself, "Why do I want a PhD? Am I willing to 'tough' it out when I'm feeling frustrated?", then you will be able to pick up right where you left off. 4) Just about EVERYBODY gets their papers rejected, even Distinguished Professors and Nobel Prize winners. My PhD advisor has co-authored over 250 papers and is quite smart, and he still has papers rejected. Professors at all levels get their papers rejected, some multiple times before they are finally published. It’s part of the process. It also happened to me for the first paper I ever submitted. Rejection always stings, but I say if it happens, take a deep breath and cool off a bit. Once you’ve acknowledged the disappointment and cooled off, read the referee reports and comments from the Associate Editor very carefully. Peer review is inherently a subjective process, but for the most part, paper referees take their jobs very seriously, and there will be valid concerns and comments for improving your manuscript (even if some might not be the most diplomatic when letting you know the faults they find with it!). It may be that the journal you submitted to just might not be the most appropriate venue for your work. Or there may be more substantive changes that are needed to make your manuscript more acceptable for publication. After my first paper was rejected, I spent a lot of time with my advisor revising it. We eventually re-worked the whole paper (e.g. cutting down the length of the literature review to the most essential points), we proved a new lemma and a new theorem that showed our new estimator’s improvement over previous estimators, and we performed several new simulation studies that showed quite interesting results. We just resubmitted this paper, making appropriate changes suggested by the peer reviewers who had rejected the manuscript, and I have to say my paper was way better than before. The paper was better off in the long-run. 5) The choice of PhD advisor is critical. It's very important that your PhD advisor is someone whom you can have a great working relationship with, whose research is interesting to you personally, and who is actively publishing in respectable journals. I think the last two are more important than anything else, especially for academic jobs. You basically need to have quality papers and excellent recommendation letters if you want to get a good postdoc or faculty position. Some PhD students are hesitant to work with Assistant Professors and are "star-struck" but there's really no point working with a world-renowned professor if their mentorship style and their research does not align with your personal working style/interests. Plus, an Assistant Professor who is actively publishing their work in top journals can still help you develop your career. Some people need a bit more guidance and an advisor who gently “pushes” them, while others can operate fairly independently and do not need to meet their advisor very frequently. The working style of you and your advisor should mesh well if you hope to be productive. 6) The fields of statistics and biostatistics change very rapidly, so it's more important that you do research that "comes from the heart" than try to keep up with a "hot area." I would not recommend researching a topic that is so archaic and obscure that only a tiny number of people in the world are still working on it. But I also think that you should prioritize your personal interests above what's currently "hot." It can be very difficult to predict what will be "hot" years from now. For example, Dirichlet processes were not very popular when the concept was first introduced, but decades later, Bayesian nonparametrics have exploded in the field of machine learning. It used to be that SVMs were very popular and neural networks lost some of their popularity, but currently, it is the opposite. There is an explosion of interest in neural networks/deep learning and not as much in SVM. The fields of statistics and biostatistics are constantly evolving and changing, so trying to "time" your thesis to a "hot area" can be tricky. But most importantly, a PhD is a very time-consuming commitment (at least 2 years of research). So you do not want to be miserable the whole time you are doing it. So make sure to pick a thesis topic that you find interesting. You probably won’t be able to do that yourself at first, but to that end, your advisor will help you hone in on some interesting open problems to work on. Do not do a topic that you have no personal interest in! Sure, some people might be more impressed if you do (what they perceive to be) a more "difficult" topic, but at the end of the day, you're the one who has to live with yourself and your career choices. And if your heart just isn't into it, it will make finishing the PhD much more excruciating. 7) Do not assume that your PhD thesis topic is the only thing you will work on for the rest of your career. To tie in with my previous point, you can always change gears and switch to a “hot” research area after you are done with your PhD. Finishing the PhD is the start of your career and certainly not where you want to peak. A PhD dissertation is usually on a specific, narrow topic or set of topics. Some people are lucky and can milk their research area for the rest of their career, but many people aren't that lucky. Even if you want to go into industry, an employer of PhD graduates is going to expect that you can teach yourself new things (new software, new models, etc.) on the fly, even if you've never seen/used these things before. In fact, it is this creativity and ability to learn new things quickly that makes hiring a PhD graduate more appealing than hiring someone with juts a Masters. Likewise in academia, professors are teaching themselves new things and moving into new areas all the time. My own PhD advisor began his career doing frequentist nonparametric statistics, but now he has research in a variety of areas of Bayesian statistics. The postdocs I am currently considering are in entirely new areas that I haven't learned before. By the end of the PhD, you should ideally have enough maturity and initiative to teach yourself different areas of statistics.
  3. Hello! I've been accepted into Columbia and UNC's Biostatistics MS programs and I'm having trouble deciding between the two! I'm leaning towards Columbia but it worries me that their Stats department is apparently not well regarded. Does this reputation extend to their Biostats department, even though their department is still in the top 10? I know that UNC is a better program overall and has more renowned professors, but if my end goal is to pursue a PhD, does it matter where I get my MS? Cost is not a strong factor because I consider living in NYC to be a once in a lifetime opportunity. I am also more interested in eventually working in industry if that helps. (I've heard that Columbia has more ties in industry and UNC is more theoretical with difficult quals) Thanks so much for any replies!
  4. Hello! I am very stuck between Michigan and Emory right now. I know Michigan has a better biostatistics program than Emory. But in terms of location, I like Atlanta way better than Ann Arbor. In addition, I think the coursework (and the Qual) at Michigan might also be much harder than Emory. My plan after graduation is to find a job (Pharma/Tech/Consulting), so will there be a huge difference between these two programs? I know PhD students at Emory can find an outside intern job during the summer. I'm not sure if I could do the same at Michigan. Any comments are very welcomed, like the pros and cons for each of them? Thanks! I appreciate that very much.
  5. Has anyone received offers from Canadian MSc programs in Biostatistics?
  6. I have heard a few people claim casually that Harvard’s biostatistics PhD program is very “applied.” Could anyone elaborate what this might mean or whether this is in fact a general consensus about the program?
  7. Where to apply?

    Hello world, I would like some help in determining where I should be applying to for PhD programs? Although I applied this year, many of the schools that I applied to I know now were "reaches." So if worst comes to worst and I do not get accepted into any of my schools or do not receive enough funding to attend any of those schools I would like to know what my options are and where I should be looking for next application cycle? I have put some of my stats below. Undergrad Institution: Large public university Majors: Statistics (B.S.) and Physics (B.S.) Minors: Actuarial Science GPA: 3.30 cumulative, 3.30 major Graduate Institution: Large public university Major: Statistics (M.S.) GPA: 3.74 Type of Student: Male, Hispanic, Domestic GRE General Test: 156V, 161Q, 4.0AW Programs Applying: PhD in Statistics (will apply), PhD in Biostatistics (may apply) Interests: Machine Learning, AI, Big Data Analysis Research Experience: 1.5 years in Physics lab programming equipment (undergraduate volunteer). 1 year in Statistics group programming in C++, and R with research into causality in a Bayesian world (graduate assistant). I may have a publication by the end of the semester. Employment: Have worked as GA during my graduate studies in a center that focuses on biostatistics and performs statistical consultations for the entire university. Consulted on many projects. Skills: R, C++
  8. Hello, I would like some insight on the difference of a 1 year program vs 2 year program. I am interested in pursuing a PhD after my master degree. However, I am not 100% sure. Which is why I am interested in pursuing a masters first. However, would it hurt my chances of getting into a good PhD program later if I choose a 3 semesters master program at a higher ranked school vs a 2 year master program at a lower ranked school? I know people have said that 2 year program typically have more theory classes than 1 year classes. However, when I've been looking at the courses for each individual program (1 year vs 2 year), they do not seem to differ by much. But, do PhD schools weigh the difference more heavily? I'm hoping someone with some experience will be able to help me as I am in the process of making my decision. Thank you.
  9. I recently received admission offer from U of Minneasota and Boston U. Both are MS in Biostatistics program. I plan to go for a job after graduation. Both programs having pros and cons and it is really hard to reach a decision. For example, the program in Boston U is only one year, and it is surrounded by so many pharmaceutical companies which suggests more job opportunities. While U of M has a higher ranking, and it is highly possible to obtain financial funding support in the second year of study. Also my family is in Minneapolis, so I can live at home and save housing expenses if I attend U of M. Does anyone familiar with these two universities? Any suggestions? Thank you.
  10. Hi everyone, I am really fortunate and have received two offers so far. One from UW Biostatistics, and the other from CMU Statistics. Even if I receive no other offers, I'll have a really hard time deciding on a program. I'm not exactly sure what I'd like to do for research, but I've been generally interested in high-dimensional statistics, statistical computing, nonparametrics, statistical genetics, causal inference, epidemiology, and a few other rather niche areas. My ultimate goal would be to obtain a faculty position, but if that market tightens by the time I graduate, I'd also be very happy to work in industry. Also, I don't think this will be an issue in either department, but I really want to be in a friendly environment that is more collaborative than competitive. I know that UW Biostatistics ranks ahead of CMU and has a great track record of placing graduates in academia. However, I feel like CMU is a slightly better (i.e. near-perfect) research fit. I hope to visit both campuses but I just thought I'd seek out advice from you knowledgeable folk first.
  11. So I received a PhD offer from UNC Biostats last week, yet the offer letter did not mention funding details and says 'funding will be determined separately'. I looked through several threads about UNC biostats from last year, and found some applicant admitted to UNC biostats PhD program seems to be unfunded (at least until last March, the date that thread was posted). I am an international student and I feel it might be more difficult for me get funded, so I am a bit worried, and wanted to ask if anyone knows about the funding opportunity at UNC. Thanks a lot!
  12. Decision Input Needed

    I was recently accepted to both Columbia and U Pitt's master's programs for biostatistics. For personal reasons, it seems that I will likely end up attending U Pitt. The question is straightforward: what is the cost of foregoing Columbia for Pitt? Any input giving info regarding outcomes for grads of both programs would be appreciated, especially from alumni. Thanks in advance!!
  13. I entered my MPH program as a Community Health concentration (or its equivalent). However, now I'm having doubts about sticking with it because I'm afraid that it's not as marketable as other concentrations (ex. Epi/Biostats). I was considering switching into Epi/Biostats, but community health is a little more interesting in me (though I do appreciate aspects of Epi/Biostats). Has anyone else been in a similar situation and can offer some advice?
  14. I entered my MPH program as a Community Health concentration (or its equivalent). However, now I'm having doubts about sticking with it because I'm afraid that it's not as marketable as other concentrations (ex. Epi/Biostats). I was considering switching into Epi/Biostats, but community health is a little more interesting in me (though I do appreciate aspects of Epi/Biostats). Has anyone else been in a similar situation and can offer some advice?
  15. Hi all, I know it's quite late to post this evaluation. I just want to make sure my decision is appropriate. Could you please give me some advice so I can adjust my application timely.. I think my math background is not really solid.. I am going to take statistical inference and some other math courses next semester. Should I add some other programs that I have a great possibility to be admitted? Undergrad/grad Institution: TOP HK University Major: Biology GPA: 3.78/4.0 cumulative, 3.82/4.0 major (Top 5%) Type of Student: International Relevant Courses: 2 Calculus courses(A+) linear algebra (A+), 2 statistic courses (A), Basic programming (A) GRE: V156 (73%) Q169 (96%) W3.5 (42%) Programs Applying: Biostatistics MS Research Experience: Several research experiences related to analyzing NGS data (2 years) Last summer worked as an RA in a renowned institute for computational/statistical genetics Recommendation Letter: Two from my department, one from a PI of my summer intern. All are very strong. Coding Experience: R, Matlab, Python Other experience: One-semester exchange in a Top30 US university Awards/Honors: Dean's List for 3 years Hong Kong Government scholarship*2 School of Interests: Columbia Yale Brown Boston UNC Upitts UW Emory Thanks so much for your help!
  16. Phd Profile Eval (stat or biostat)

    I would really appreciate it if someone could look over my stats and give me some advice on how to look like a more competitive applicant. I'm set to graduate in 2 semesters, but I might take an additional semester if it's helpful. I plan on applying to programs next fall, and I am not sure how to proceed from here. I would also like to know if an MS is maybe more appropriate for someone like me. Undergraduate Institution: University of Georgia (top 20 public school) major/minor: Math/statistics GPA:3.67 Ethnicity/Gender: White Male GRE: Haven't taken it yet Math GRE: I don't want to take it unless I need to take it Programs Applying: Statistics phd or biostatistics phd Noticeable Courses Taken: Calc 2 & 3 A- , Intro to Proofs A linear algebra A, Abstract Algebra B-, Differential Equations B Sequences and Series (Introduction to Real Analysis) B Real Analysis (Don't know yet, likely a B or C) Complex Analysis (Don't know yet, likely a B or a C) Intro to Statistics A- Statistical Methods B+ Intermediate Biostatistics B+ Some classes I plan on taking are a follow up course in real analysis, number theory, (maybe abstract algebra II?), CS courses (including a stat programming course), and other stat courses to finish my minor. Is that a good approach? Awards: Made Dean's list spring my freshman year, because I had a 3.9 overall GPA Research experience: Nothing Notable, but I've talked to a professor about working on it, I plan on applying to an REU Misc: I have three semesters left before I graduate, so I need advice on how to make the most of it. I just don't really know what I should be looking at. My transcript looks a little off, because I made A's and A-'s my first year of college, and then I did alright my third semester besides the B in differential equations, but I made nothing but B's my fourth semester, because I took nothing but math and statistics courses, and I had an unusually bad experience with Algebra (I believe the teacher was reprimanded for the course, because it was absurdly difficult) so it caused my whole semester to just go off the rails. This semester I've been dealing with a continuation of extenuating circumstances involving my personal life that have caused me to not do as well as I could have this semester. I also find Real/Complex Analysis to be very difficult subjects. Schools and programs I am applying to: Probably UGA, maybe GA Tech, not sure where else to realistically apply at If I don't get funding, I can't really afford to go anywhere Concerns: My GPA is probably going to go down after this semester, and it's starting to get into a low range. My grades don't look great, but I can explain sort of what was going on in my life to cause that to happen. I don't really have a lot of research experience, and I'm not sure I'll stand out as an applicant to any programs.
  17. Although quite a few applications are due tomorrow there are still a handful of schools with deadlines a week or more away. Would anyone applying to PhD programs to statistics or biostatistics feel like swapping statements of purpose for another set of eyes to give feedback
  18. MS Biostat Evaluation

    Undergrad Institution: Large state School Majors: Mathematics, Economics GPA: 3.55 (Math: 3.3, Economics: 3.85) GRE: 162 Q, 157 V Type of Student: White male Research Experience: one semester MCMC statistical analysis with risk management professor (who holds a Phd in Economics from Stony Brook and Phd in Risk Mgmt from U Penn) Awards: Dean's List (three semesters), Hollingsworth Award (best in class for linear algebra) Jobs and Activities: Volunteer experience (IMPACT), Math tutor (1.5 years) Coursework: Math: Precalculus (D,A), Calc I (A), Calc II (C+), Calc III (A), Differential Eqs. (A), Proofs (B+), Linear Algebra (highest grade), Intro to Real Analysis (A-), Probability (A-) Stat: Intro to Stat (B+), Stat Methods (A), Econometrics (A), Time Series Econometrics (A-) Biostat: Taking med level biostat next semester Recommendations: Prof mentioned above (Strong rec), Prof with whom I took two upper division math courses (Medium rec, Harvard Grad, Renowned in field), Prof with whom I took econometrics (Medium rec) Coding Experience: R, Matlab This is my first post here. If anyone has any insight on institutions to which I might be accepted, any help would be greatly accepted. Thank you so much.
  19. Undergrad Institution: Big State School Major(s): Industrial Engineering Minor(s): Mathematics GPA: 3.31 Industrial Engineering 2.88 in Mathematics (Freshman Calculus Sequence is really killing me here, chalk it up to youthful ignorance) Type of Student: Domestic Asian Male Courses Taken: Calculus I (B), Calculus II (C), Calculus III (B), Applied Linear Algebra and Differential Equations (A), Discrete Math (A), Probability (A), Statistics (A) , Operations Research I (A-), Operations Research II (A), Statistical Process Control (A), Discrete Event Simulation (B+), Engineering Design of Experiments (A), Physics (all A's), Vector Statics/Dynamics (C/C, by far my weakest subjects), Digital Logic(B-), Strength of Materials (C), Properties of Materials (A) Courses in Progress: Theoretical Linear Algebra, Applied Regression, Upper and lower division Numerical Analysis Courses I will be taking in the future (These aren't gonna be on my app before applying): Undergraduate Real Analysis, Graduate Generalized Linear Models, Graduate Numerical Linear Algebra, Stochastic Calculus or Abstract Linear Algebra, Intro to ML GRE General Test: Q: 163 (84th percentile) V: 166 (97th percentile) W: 4.0 (60th percentile) GRE Subject Test in Mathematics: No plans on taking it Programs Applying: A mix of Stat, OR and Biostat MS/PhDs Research Experience: n/a (probably a dealbreaker for PhD programs?) May have some work beginning in January, but would be irrelevant for apps. Awards/Honors/Recognitions: Dean's list a few times, but nothing of note Pertinent Activities or Jobs: Did a fair amount of engineering work in industry. Had an 8 month full-time co-op with a major medical device manufacturer, did a major facility redesign at a different medical manufacturer for senior design project, recently had a process engineering internship at a major hospital provider. Letters of Recommendation: Two letters confirmed, one PhD in Biostat the other in Systems Engineering. No idea of quality though; scrambling to find third. No response from faculty member who knows me fairly well, so not hopeful on that front Any Miscellaneous Points that Might Help: I'm mostly interested in pursuing Bayesian methods and computational stat with side interests in ML and stochastic optimization Applying to (far from complete, please let me know if you have any suggestions, this needs to be pruned down): USA: NC State (MSc, Operations Research) OSU (MS, Statistics) Penn State (MS, Statistics) Purdue (MS, Industrial Engineering with OR focus) Purdue (MS, Statistics) UCLA (MS, Biostatistics) UCI (PhD, Statistics) UCR (PhD, Applied Statistics) UIUC (MS, Statistics) UNC (PhD, INSTORE) USC (PhD, Operations Research) USC (PhD, Biostatistics) UC Berkeley (PhD, IEOR) (waste of time?) UCSB (MS, Statistics) UMass Amherst (MS, Statistics) University of Michigan (MS, Statistics) University of Wisconsin at Madison (PhD, Industrial Engineering) UT at Austin (MS, Operations Research) UT at Austin (MS, Statistics) Canada University of British Columbia (MSc, Statistics) Simon Fraser University (MSc, Statistics) Simon Fraser University (MSc, Mathematics (with focus on Operations Research)) University of Alberta (MSc, Statistics) University of Waterloo (MMath, Biostatistics) University of Waterloo (MMath, Statistics)
  20. Undergrad/Graduate Institutions: University of Texas at Austin Major: Mathematics Cumulative GPA: 3.82 cumulative; 3.75 in major Type of Student: Domestic White Female Math/Stats Courses: Multivariable Calculus (A-), Probability (A), Linear Algebra (B+), Mathematical Statistics (A), Stochastic Processes (A), Applied Regression Analysis (A), Biostatistics (A), Real Analysis (A), Differential Equations (B) Got credit for Calculus I and II so hopefully that doesn't matter too much considering I've needed calculus in nearly every class since? Also going to graduate with 18 hours in CS classes (data visualization, basic programming in python, databases, mobile computing and numerical analysis) Quantitative/ Programming Courses: Elements of Computing (A) - this was basic programming in Python, Elements of Software Design (A) - more Python, Elements of Data Visualization (A) - included SQL, R, Tableau Elements of Databases (in progress) - SQL, Python, BeautifulSoup, learning to understand how cloud services such as AWS work 2 more classes next semester: Numerical Analysis in C and Mobile Computing where we will have to create a fully functioning app. I feel very confident in RStudio and pretty good about SQL as well. I think my coding in Python is decent but could definitely use some brushing up. GRE: Just took today and am incredibly upset. Quant: 160 - obviously don't have the percentages yet but this is much lower than I planned. This was my only run and I had a lot of anxiety surrounding it. Just couldn't get in a clear headspace due to anxiety. Verbal: 160. Don't have analytic score yet but I think it'll be > 4.0 at least. My first essay was pretty mediocre but I felt my second one was pretty good. Research Experience: 125 hours over the summer in a Human Development and Family Sciences lab; worked on coding in SAS and SPSS to calculate BMI percentiles for the teenagers in the study; also helped organize a small team to put together data for the professor's grant analysis. Recommendations: 1. The professor I worked with over the summer. She was surprised I completed the BMI percentiles task and said she expected that from a graduate student. 2. Applied Regression Professor - feel like it will be good? He said he'll discuss how I did in the class and my work ethic (i'm hoping the second part is what really stands out) 3. Stochastic processes professor who is very well-known; I can't say it'll be very personal or good but I hope that's offset by others who I feel know me more personally. 4. My Real Analysis professor. I had him for a class over the summer and he's just a really sweet guy. It might not be glowing, but I feel he can attest to my mathematical ability due to my work in the course. Other tidbits: I have worked about 20-30 hours per week consistently at my part-time job as a front desk attendant at a recreation center. This isn't really all that relevant, but I included how my daily encounters with the homeless people there have motivated me to pursue biostatistics in my personal statement. It also explains some Bs during my sophomore year as I was getting used to working. I'm mildly concerned about the B+ in linear algebra; however, I am taking Applied Linear Algebra this semester to brush up on those skills. Currently a TA for Biostatistics where I grade and assist with lecture and lab. Programs I'm applying to based on order of preference: University of Washington (biostatistics) (MY DREAM SCHOOL) but I'm feeling very discouraged atm with getting in due to my low quant and I won't have time to retake . University of Wisconsin-Madison (biostatistics) UT Health Science Center (biostatistics) Rice University (statistics) Oregon State University (statistics) Colorado State University (statistics) University of NC - Chapel Hill (biostatistics) Baylor (statistics) University of Texas Dallas (statistics) * Some of these schools only have a statistics program but with biostatistics electives; I marked whether I'm applying biostatistics or statistics. Thank you so much for reviewing. Please let me know if I should consider any other schools/drop some of these because honestly if it isn't necessary to keep schools like UT Dallas and Baylor I'd be thrilled with dropping them. Lat note: I won't have time to retake the GRE for my top 3 schools (of course >:( ); however I will be retaking it ASAP for other applications.
  21. Undergrad Institution: Small Private Liberal Arts College Major(s): BS in Biostatistics GPA: 3.514 overall (Biostats major (math,bio,chem): 3.29, math classes only: 3.63) Type of Student: Domestic Male GRE General Test: Q: 156 (63%) V: 167 (98%) W: 4.0 (59%) GRE Subject Test in Mathematics: N/A Research Experience: 1) Summer doing research with Mathematics Department, focusing on developing a model of migratory species. Used Matlab. 2) Summer REU in Pittsburgh doing data analysis of enhancer sequences. Used R. Courses: Calc I,II, III (B, A, A), Linear Algebra (B+), Applied Statistics (B), Advanced Statistics (A-), Probability (A-), Discrete Methods (A), Mathematical Modeling in SCI (A), Bio 1105 Intro to Molecular Bio (B+), Bio 1106 Evolutionary Bio (A-), Genetics (B+), Chem 1101 (B), Chem 1102 (C+) Letters of Recommendation: PI at REU (good), Undergrad math/biostats advisor (good), 2 math professors I took classes with (good, but only one class with each) Work experience: ~3 years working on campus in customer service position, ~1 year at Dana Farber Cancer Institute doing data entry Awards: Deans list, nothing significant Applying to: MS Biostats/Stats/Data Science (realize that last might be better considered in the CS forum, feel free to focus on the other two) MS: Northeastern (goal, stats or data science) Boston University (stats) Umass Amherst (stats or CS w/ conc in data science) University of Pittsburgh (biostats) Worcester Polytechnic Institute (data science) Concerns: 1. Low GRE Quant score 2. Lower score in biostats courses 3. Low Bio/Chem scores I recognize I have a weird situation here and would really appreciate any advice/insights. I'm applying to MS programs in Stats/Data Science/Biostats (all three!), please feel free to only focus on those your familiar with! Order of preference is Data Science > Stats > Biostats but wouldn't mind any of the three I thought I'd apply to all three types of programs and see what I get accepted to. I'd love Northeastern: its local, offers data science as an ms, and is in walking distance from an internship I plan to apply to. Biostats MS programs I'm a bit iffy about applying to, I just am not that good at Bio/Chem (see my overall biostats gpa...) and math interests me more. More a question of whether it is realistically worth applying to though, since I don't mind context I work in so long as I'm doing math! Thanks a ton in advance to anyone who responds *edited for clarity
  22. Undergrad Institution: Top 30 US University (Top 6 Public) Major(s): Applied Mathematics (minor Public Health and have taken computer science classes) GPA: 3.87/4.00 Type of Student: Domestic Asian female Relevant Courses: Linear Algebra (A), Probability Theory (A), ODEs (A), Foundations of Math (A-), Operations Research: Stochastic Models (A), Data Analysis (A), Multivariable Calc(A), Statistical Data Mining (a grad class- A), Data Structures (A-), Mathematical Statistics (A), Foundations of Epidemiology (A). While I studied abroad (National University of Singapore), I took Statistical Methods in Epidemiology, Survival Analysis and Numerical Analysis. I withdrew from Real Analysis last semester because I had mental health problems from reverse culture shock after coming back from abroad, but I am retaking it this semester and doing much better. I am also taking a graduate theory class (Applied Linear Regression) and 2 graduate classes next semester. GRE General Test: Q: 161 (78%) V: 159 (85%) W: 4.0 (60%) (retaking the GRE in 2 weeks) Programs Applying: Biostatistics PhD Research Experience: Internship in the Biostatistics branch at the NIH (writing a paper for publication), SIBS, EXTREEMS-QED (and NSF undergraduate REU at my host university doing a computational biology project), research assistant in the math department for over 2 years and the project is now my honors thesis. I presented this project at my school's research showcase and am presenting at conferences next semester. Pertinent Activities or Jobs: Founder and President of the ASA student chapter at my school (we have no statistics department so I wanted to introduce a way for people to become more exposed to statistics, I also teach SAS and R sessions to other students at my school). Letters of Recommendation: My honors thesis advisor, my NIH research advisor (she was a former ASA president and very well known), and my faculty advisor/math stat professor. I expect them to be strong. Programs I'm applying to (all Biostat Phd): Harvard John Hopkins Emory Boston University UW UNC Columbia NC State Brown Problems: My GRE Q is low because I freaked on test day although I would get higher scores on the powerprep gre tests (168) at home. Will a 161 completely discount my profile? In addition, although I am retaking real analysis I did W from it because last semester was not the right semester for me to take it. Is my list aiming to high? Do you think I have a chance in even one school?
  23. Undergrad Institution: Small Private Liberal Arts College Major(s): BA in Mathematics and Biology (two majors) Minors(s): Computer Science, Statistics GPA: ~3.35 (Math major: 3.45, Bio major: 3.19) Type of Student: Domestic Male GRE General Test: Q: 163 (84%) V: 164 (94%) W: 3.5 (42%) GRE Subject Test in Mathematics: N/A Research Experience: 1) Summer internship at University of MN with bioinformatics research team 2) Currently doing cell biology/bioinformatic research at undergrad institution (2 months in) Courses: Calc 2 (B), Multivariable Calc (B), Linear Algebra (B), Math/CompSci Proofs (B), Math Model Building (A), Scientific Computing (A), Modern Algebra (B), Nonparametric Stats (A), Parametric Stats (IP), Dynamical Systems (IP), Comp. Sci I (A) and II (A) - not sure if these are relevant Letters of Recommendation: Professor/Supervisor at internship listed above (medium-strong), Undergrad math advisor (strong), Undergrad advisor (strong) Work experience: ~1 year as a Calculus TA/Tutor at undergrad, worked for 2+ years over holidays at construction office doing IT work Awards: Honorable Mention (2017), Meritorious Winner (2016) in COMAP Mathematical Contest in Modeling Applying to: Mostly MS Biostatisics Programs MS: UMinnesota (goal) U Washington UW Madison (Biomedical Data Science program) U North Carolina UCLA Columbia Concerns: 1. Low Analytical Writing Score. I'm fairly confident my actual writing isn't representative of that score, so I'm hoping my statement of purpose will alleviate some doubt there. 2. Low grades in core math classes As I have narrowed down my options in the past few months, I am struggling to determine how competitive of an applicant I actually am. I am feeling like a weak candidate at many of the schools I have listed and would like to add one or two more schools to that list that I would be a competitive applicant at. Thanks for any input.
  24. Hello, stat forum members! I hope some of you would be kind enough to go through this post. Undergrad Institution: India. Not one of the IITs. Major(s): Bachelors in Pharmacy GPA: ~3.4 Type of Student: International Student GRE General Test: Q: 166 V: 166 W: (waiting for official scores) GRE Subject Test in Mathematics: N/A Research Experience: No relevant research experience, I think. 1) Worked one summer in the chemistry lab. No publications. 2) Conducted a study in which I set out to evaluate the influence of personality traits on patient adherence. Unpublished but had an oral presentation in college. Not sure if it counts. Courses: Calc 1, 2 (A+)(covered in a one-semester course), Applied Math and Pharmaceutical Statistics(diff eq, laplace transformations and biostatistics) (A-) Computer Science (A), Operations Research (A+), Marketing Research Methodology (B+) Letters of Recommendation: One from calc professor and one from my chemistry lab advisor. Work experience: Have interned at pharmaceutical companies (one month in a production plant, and 4 months in the supply chain department). Not sure how much weightage that holds (if any) Applying to: MS Biostatisics Programs MS (tentative): U Columbia (Reach) U Minnesota (Reach) UT Health Science Center BU (MA Biostatistics) (Reach) Pittsburgh Vanderbilt (#1 choice overall) George Washington University (Reach) Ohio State University UIC Oregon State (MPH Biostatistics) Concerns: 1. Unsure if I my coursework meets the pre-requisites. 2. No research experience. 3. Obscure undergrad institution, bachelors in an unrelated field. I am aware that my overall profile is quite weak. I have been studying stats/biostats on my own but obviously I have quite little to show for it on paper, and of course programs would be (rightfully) skeptical of someone who claims to have "self-studied" a subject. I would really appreciate if someone could honestly evaluate my chances of getting into the universities listed above. Should I aim at even lower ranked programs? Should I wait one year and try to beef up my application (by perhaps giving the subject test)? In what other way can I improve my application? Thanks!
  25. I will be applying for Masters (and possibly some PhDs) in Statistics for Fall of 2018. I was hoping to hear how competitive my profile is at some of my target schools. Stanford (MS) is my dream school, so I'm most interested in feedback there. I'm concerned about whether to retake the GRE, since the average listed score at Stanford is a perfect score (97%). Undergrad Institution: Top 5 Public Ivy (Ranking: ~top 30-35 nationally overall, roughly top 20 in stats) Cum GPA: 3.89 Major: Statistics (3.84*), Psychology (4.0*), both B.S. *Only explicitly includes stats/psych department classes, respectively. Excludes math, astronomy, physics, & related classes that counted towards my majors, which would likely bump up my stat GPA a bit. Type of Student: Domestic White Male (DWM) from the south. GRE: 167 (93%) Quant, 164 (94%) Verbal Research/Work Experience: ~1.5 years as a statistician at a large research company. Have worked on surveys / projects with institutions such as the Bureau of Justice Statistics (BJS), Center for Disease Control (CDC), and other government bureaus. Project topics include criminology and victimization, drug usage, and general health. I presented at a large statistical conference recently (published a proceedings paper). I did some very minor research during my undergraduate classes. Awards/Recognition: Phi Beta Kappa. Dean's List GPA (3.5+) every semester. Inducted into Phi Sigma Pi Honors fraternity. Nominated for junior statistician award by coworker (winner not announced yet). Applying to: Statistics/Biostatistics, Masters (maybe PhD) Dream: Stanford (M.S.) - Statistics Reach: University of Chicago (M.S.) - Statistics** Harvard (M.S.) - Biostatistics** UC-Berkeley (M.A.) - Statistics Match: University of North Carolina (M.S.) - Biostatistics** University of Michigan (M.S.) - Statistics** NC State (M.S.) - Statistics (also looking at Advanced Analytics/Data Science) **May consider PhD, depending on how competitive I am at program