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Found 315 results

  1. Hi all! I will be applying for to MS Statistics/Biostatistics programs in US and canada.My end goal is to finish my masters and go for a PhD in Statistics. Undegrad Institution : University of Delhi (tier 2 college) Major : Economics GPA : 7.12/10 (3.6 US equivalent) Releveant Courses : Math/Statistics : Math Econ I (7) ,Math Econ II (8) , Statistics (7), Introductory Econometrics (8), Applied Econometrics (10) All grades are out of 10, the two math courses covered some linear algebra and the rest was multivariable calculus but did not have vector calculus stuff like green's theorem. The statistics course was calculus based and had random variables, hypo testing, clt and similar stuff. Econometrics was mostly regression stuff. Graduate Institution : Indian Statistical Institute Major : Quantitative Economics ( MS in QE) Grade : 86/100 (This is only one semester, I rank 4 in a batch of 23.) Relevant Courses : Math Methods (95), Statistics (95), Game Theory (91), Probability Theory (66) All grades are out of 100.Math course had good amount of linear algebra, decent amount of real analysis and a lot of optimization topics like KKT. Statistics was taught from Casella & Berger, was very mathematical had things like cramer-rao bound, cramer woldt device, likelihood ratio test, NP lemma etc, Prob Theory was from the stat department and I couldn't do well in it because Real Analysis was a prerequisite which I had not studied, the course had convergence concepts(almost surely,distribution etc) and markov chains. Relevant courses from this semester are Econometrics I, Theory Of Mechanism Design( not sure how relevant but it is a mathematical course) and Game Theory -II When the time to apply comes I will have had a course in Real Analysis(Analysis-I from the stat dept) and I am pretty sure I'll get an 80+ in it and I will also have had and additional course in Econometrics, one in Sample Survey and and one in Time Series, in my last semester I will also take either Measure Theory or Analysis - II depending on my interest, I cannot take these now or in the next semester as the Institute doesn't allow too many courses from other dept. in one semester. General GRE : Quant(165), Verbal(164) GRE Mathematics Subject Test : Not given yet. The things that I worry about are that my UG institute wasn't really the best in the country, it wasn't a bad school but just wasn't amongst the top colleges, my GPA in UG is also not fabulous however I think it is still better in the relevant courses. Will bad grade in prob theory be a significant factor? I am also planning to give the GRE Math by studying for it over the summer, I think I should be able to manage 70+ percentile on this, will this add significant value for top schools because if not then I could intern over the summer and make good money. LOR : One will be from my game theory and Mechanism Design professor who is a PhD in industrial engineering, rest all will be from economists though should be good. My dream programs are Stanford MS Statistics, U of Chicago Stats, Harvard Biostats, CMU,U of Washington. For canada I know that U of toronto does not take international students so I was thinking about U of waterloo and UBC. Can you guys tell me about my chances at these schools and also the schools that I should actually apply to . Also what else can I do to improve my chances of getting into the best programs, and should I do a not stats relevant internship in the summer or give the GRE Math, sorry for too many questions.
  2. Someone helps me! I am so hesitating! I will list pros and cons of these two programs and plz vote! 1. Columbia University Teachers College Applied Statistics pro: It's location is the best. CU is my dream school. con: TC is so independent and I am afraid of being questioned as a student of CU. Also this program is often being criticized. 2. UPenn GSE SMART(Statistics, Measurement, Assessment, and Research Technology) pro: GSE is better than TC. con: Location is not as good as NY(for me). Please share your opinions!
  3. Someone helps me! I am so hesitating! I will list pros and cons of these two programs and plz vote! 1. Columbia University Teachers College Applied Statistics pro: It's location is the best. CU is my dream school. con: TC is so independent and I am afraid of being questioned as a student of CU. Also this program is often being criticized. 2. UPenn GSE SMART(Statistics, Measurement, Assessment, and Research Technology) pro: GSE is better than TC. con: Location is not as good as NY(for me). Please share your opinions!
  4. Someone helps me! I am so hesitating! I will list pros and cons of these two programs and plz vote! 1. Columbia University Teachers College Applied Statistics pro: It's location is the best. CU is my dream school. con: TC is so independent and I am afraid of being questioned as a student of CU. Also this program is often being criticized. 2. UPenn GSE SMART(Statistics, Measurement, Assessment, and Research Technology) pro: GSE is better than TC. con: Location is not as good as NY(for me). Please share your opinions!
  5. I have been accepted to PhD programs for the upcoming Fall at the University of Florida (UF) and the Iowa State University (ISU). -> UF has a small program with younger faculty--most of them are recent graduates from ivy schools--while ISU has one of the largest Statistics department in the US. ->UF is ranked #40 this year and ISU is tied at #20 -> Both places have research that interests me -> UF has a better reputation as a research university in general Please help me decide on picking the school for myself. What factors should I consider before making a decision? Are there any current graduate students or alum from these schools who can give some pros and cons on the schools?
  6. I know that internal fellowships tend not to matter much beyond the period of time in which you have them. I've been offered two rather different fellowships, however, and I'm trying to determine if the difference between them is substantive or not. The first is UNC's Royster Fellowship, which comes with about a $6,000 stipend increase. It seems rather prestigious and selective, with the fancy name, university-wide competition, and inclusion into the "Royster Society of Fellows," with lots of different networking and professional development opportunities. My guess is that all of this apparent prestige will only matter while I'm at that particular university, if at all. So, Question 1: Do people outside of UNC (in academia or industry) know about (or be impressed by) this so-called "Society of Fellows," or is it just fancy window dressing on a (more competitive) funding award? The above fellowship also includes two years without duties--one during my first year of intense coursework and the other in my fifth year during my dissertation work. (So from years 2-4 I would work as a TA/RA, but with a guarantee of the same level of funding as in years 1 and 5.) Question 2: As someone going into a STEM field, how much help is a fifth year fellowship? Everyone else in the program is already guaranteed TA/RA funding for 5 years. Would not having work duties be instrumental to finishing up my research in my fifth year, or just a convenience? (I'm sure that this will depend on the progress of my research and how early I start, but any input would still be appreciated.) Finally, my other big offer is a rather bland-sounding college-level "Dean's Fellowship" which comes with a larger funding increase--around $13,000 more than my initial offer from that department. Only the first year has no teaching duties, however--for years 2-5 I'll have to work as a TA/RA but for the same increased stipend level guaranteed. While both departments are undoubtedly trying to recruit me, my gut feeling based on the fellowships is that I would be more valued at UNC. It seems as though their STOR department has had only one other Royster fellow in the past five years, whereas I get the sense that the other department's fellowship is much more common and less of a "big deal". Question 3: Is this thinking completely illogical? Does the difference between the level of fellowships each department nominated me for give any signal as to how valued I would be by the faculty and department? Does getting a more competitive fellowship inherently mean I would be a "bigger fish" in that program, or should I just take things at face value? (Admittedly, trying to gauge how desirable I am as a candidate after I've been accepted might be a bit of a waste of time, but I'm self-aware enough to know that feeling valued by a given department is important for my productivity, self-esteem, and long-term academic success.) I would greatly appreciate anyone's thoughts on any of these questions. (Or people to tell me to quit being silly and obsessing over trivial differences, if it turns out that the answers don't really matter.)
  7. Undergrad institution: big U.S. state school with decent math department Majors: Double Degree with BS in Math and BA in Econ GPA: 4.0 / 4.0 (both major and overall) Type of student: International (White male) Courses taken: Math: Basic: Calc I - III (A/A/A), Linear Algebra (A), Ordinary Differential Equations (A) Advanced: Abstract Linear Algebra (A), Abstract Algebra I - II (A/A), Mathematical Analysis I - II (A/A), Numerical Analysis (A), Intro to Partial Differential Equations (A), General Topology (A) Stats: Probability and Statistics (A), Mathematical Statistics (A), Stochastic Processes (graduate credit: A) Programming: College courses: CS Java course (A), CS Python course (A) Coursera online courses: C++ course, Algorithms and Data Structures, Machine Learning: Supervised/Unsupervised + intro to Hadoop/MapReduce/Spark Courses will take: Real Analysis (graduate credit), Mathematical Economics (graduate credit), Calculus of Variations (graduate credit), Differential Geometry Recommenders: Math professors, well-known in their respective areas, with whom I have good personal contacts Research experience: This is my weakest point, since I have not been able to do any particularly notable research as an undergrad. I applied and got accepted to REU this summer but could not attend due to family reasons. At the department level, I tried doing research with one of my professors in statistics, but he left soon after, so the paper was never finished. Work experience: Financial Analyst Intern (Summer 2018), Data Manager Intern (Summer 2019) Awards: Economics and Math Department Scholarships, President's Honor Roll for every semester GRE General: 157 (V), 167 (Q), 4.5 (AWA) GRE Math: Taking this fall School list: Need advice on where to apply. One of my friends suggested that I should apply to schools where my professors got their PhD's. But other than that I don't even know which tier to aim. My biggest concern is the lack of research experience. Masters is not an option, since I just can't afford it right now + I am on my national government grant.
  8. I am not MFE concentration so I don't know how good is Applied Operational Researches concentration. UW is known for their stats(correct?), but I have heard few comments on this program. My main concern is job opportunities as I intend to work in the Stats after graduation
  9. Hi everyone, I just received the PhD offer from CMU stats department, and I am so happy with that. However, now I am facing a difficult decision between choosing to stay at University of Toronto for my PhD (where I did my undergrad) or go to CMU for a new adventure. My current research interest is more about: 1. Applied Bayesian inferential methods. 2. Statistical Computations. 3. Machine learning and Data Science. And my future plan is to find a faculty position in statistics. The pros for UofT: 1. I am an international student, so staying at uoft makes it more possible for me to get the PR card in Canada, while the PR card in US is a bit impossible... 2. I did my undergraduate study at here, so I know the faculties at this university very well. And I have been matched with my preferred supervisors whom I have already worked with and felt good working with, while CMU currently did not match me with any supervisor yet. 3. There will be five years of full funding package which includes full-tuition plus 20k stipend per year for UofT, while CMU's offer only describe my full funding package for one year (full tuition + 3.1k stipend per month), and based on their student handbook it seems like the funding from CMU usually only lasts for four years... The pros for CMU: 1. Higher ranking in Statistics, especially in Machine learning and Data Science. 2. Based on the suggestions from my professors, if I want to continue my career in academia, it seems like it would be better for me to not go to the same place for both undergraduate and PhD... But I am not sure how important that factor is 3. Probably the winter at Petersburg will be more approachable than in Toronto... Could anyone give me some suggestion on how to make this choice? Any suggestions is appreciated! Thanks so much!
  10. Hi all, I'm planning on applying to statistics PhD programs for next cycle (entry in Fall 2021) and would love an honest assessment of my profile, with strengths and weaknesses, and recommendations on programs to apply. My interests broadly are statistical machine learning with applications to healthcare and social sciences! Undergrad Institution: Top LAC Majors: B.S. Mathematics and Economics GPA: 3.97/4.0 Type of Student: Domestic White Male GRE General Test: Q: 170V: 170W: 6.0GRE Subject Test in Mathematics: 760 (71%) - April 2019 Programs Applying: Statistics PhD Research Experience: In my job since graduating college I have been working on health economics and epidemiology studies, and have a number of co-authorships on publications (although not directly related to statistics). Hoping to move away from biostatistics even though it's more relevant to my current work experience (I am aware biostats and stats departments are very similar!). I also presented an economics research paper at a well known economics conference with my thesis advisor from school since we wrote a continuation of my thesis after I graduated. Awards/Honors/Recognitions: Summa cum laude, award for top economics student in graduating class. Pertinent Activities or Jobs: 1.5 years of research experience in health economics and epidemiology in my job. Letters of Recommendation: I have two math professors who know me well and said they would be happy to write me strong letters (both of them offered when I mentioned I was thinking of applying to PhD programs), one of them very well known in their field. The other is my thesis advisor from the economics department. None of them strictly from statistics department, but hopefully they are relevant! Coding Skills: R, Python Relevant Classes, Grades: All undergraduate level 😕 Mathematics: Statistics (A); Multivariable Calculus (A); Linear Algebra (A); Discrete Mathematics (A); Mathematical Modeling and Computation (A+); Data Mining and Machine Learning (A); Financial Calculus and Probability (A); Real Analysis I (A+); Real Analysis II (A); Abstract Algebra I (B+) Other Relevant: Introduction to Programming with C++ (A). Advanced Econometrics (A). Comments: 1. I think that my biggest weaknesses are my lack of graduate classes (I added a math major very late in my career at the recommendation of one of my letter writers from the math department) and while I have research experience in publications in a related field, none of it is directly statistics. Also, I took the math GRE after a year out from college and was definitely rusty - I feel like it's a respectable score but am unsure if it will help or hurt my application for those that don't explicitly require it (i.e. not Stanford!) Could you guys help me get some clarity on that? I know it's a hotly debated subject (pun intended) on this forum! 2. One recommender said I would have a solid shot at some top 10 programs but I am not feeling as confident - what do you guys think? I will definitely have a few "shoot for the stars" schools on my list that really align with my research interests of machine learning and social science applications (Berkeley, Harvard, Michigan) but I need to add some more realistic schools I think, unless you guys feel otherwise! Thanks you all so much for reading - I've learned a lot from going through this forum and would love your guys' input!
  11. Hi all, I'm a senior undergrad in the US graduating in the spring. I'm applying to PhD programs in statistics for fall 2020. I posted my profile recently and got great feedback, and I was wondering about a few more things: 1. I changed the list of schools I'm applying to, do they seem reasonable? I wanted to apply to more reach schools to give myself a better chance of getting into one, while also applying to enough lower ranked schools to not feel worried about getting into zero schools. 2. If I'm okay with doing a master's instead of a phd(in preparation for a phd), in general will it hurt me to apply for the phd programs? I.e. are there schools where you would get rejected outright(not even offered a master's) if you applied for the phd program but where you be accepted had you only applied for the master's? Undergrad Institution: Large public university ranked top 20 in math by US news Major(s): Applied Mathematics GPA: 3.89 GRE General Test: Q: 166 (89%) V: 165 (96%) W: 4.0 (57%) Math Subject Test: (probably won't take) Programs Applying: Statistics PhD/M.S Research Experience: Did a project related to Dynamical Systems with an applied math professor, and this summer have been doing a project with a statistics professor that I'll continue into the fall semester. Letters of Recommendation: Two from the professors I did projects with. The applied math professor will probably write an at least ok letter and I think the stats prof will be able to write a strong one. I'm going to ask a physics prof I took quantum mechanics with for the last letter because I did very well and I thought he liked me. Grades: Mathematics: Calc III(took in hs and it stayed on my transcript :///) (B), Linear Algebra and Differential Equations(honors and proof based) (A), Multi-Variable Calculus(honors and proof based) (A), "Applied Mathematical Analysis" which was a mixture of harder calc III and some complex analysis(honors) (A), Intro. to PDEs (B), Analysis I, II (A, A), Intro. to Measure Theory and Integration (A-), Intro. to Probability Theory(proof based) (B), Modern Algebra I, II (A, A), Numerical Linear Algebra (B+), Intro. to Mathematical Optimization (A), Intro. to Combinatorics (A) Computer Science: Intro. to programming (A), Data structures (A) Physics: 3 intro classes (A,A,A) a lab course (A), quantum mechanics (A), thermodynamics (A), classical mechanics (A), electrodynamics (A) Additional courses I will have taken before applying: Numerical Analysis, Stochastic Processes Schools I'm thinking about applying to:(For statistics phd unless otherwise indicated) "Safety"(not really of course): Iowa State, Madison, Illinois UC, Purdue, John Hopkins(applied math/statistics) Reach: Chicago, Columbia, U Washington, CMU, U Penn Any suggestions/feedback would be great! Thanks
  12. Hello! I am applying for statistics M.Sc programs. Would anyone be willing to look over my statement of purpose? If you are, please PM me!
  13. Hi, I correlated all my study variables, and some of the demographic variables, with each other, to see if there were any significant associations. I found significant correlations between some study variables with demographic variables. For example, let's say I analyzed which type of candy participants like eating the most; and the amount of candy XY eaten correlated with participant's educational status. This would seem like a "spurious" association, as in there would be no obvious explanation why participant's education should be associated to how much of candy XY they eat. My questions: 1) Is it common to to this sort of preliminary correlational analyses to explore associations between variables? 2) Should I report significant correlations, even if they are not part of my study questions/hypotheses? 3) If yes, should I mention these significant correlations as well in my discussion? Or can I simply report them in my results part, and them not mention them anymore in the discussion? Thank you in advance !
  14. Having read the pinned post on research statements, I am still feeling uneasy about indicating potential advisers in my statement of purpose. The conventional wisdom in just about any other field seems to be that you should reach out to and engage in a dialogue with potential advisers before writing your statement of purpose . Is that really not the case in Statistics? I've tried emailing a few professors whose research called out to me, but I haven't had a response. I'm wondering if I need to write more compelling emails, or if this just is not an approach that works in Stats. So far, only a few of the programs I'm applying to ask you to specifically indicate who you'd like to work with - should I do it anyway for the rest? Is it risky if I say that I'd be interested in working with someone and it turns out that person isn't taking grad students currently? It feels like a difficult balancing act: I want to write with enough specificity to demonstrate maturity in the subject, yet I don't want to pigeonhole myself. Thank you!
  15. I am very interested in pursuing biostatistics PhD for this coming year (2020). I realize I have a low gpa score but have a strong research record/work experience to compensate. I would really appreciate honest feedback about my chances for graduate school this coming year given that I can get very strong LORs. Undergrad Institution: Top 10 LAC Major: Computer Science and Statistics GPA: 3.56 Student: Domestic POC, female Courses: Intro to Stat Modeling (A-), Intro Computer Science I (B+), Multivariable Calculus (A-), Intermediate Statistics (A-), Intro Computer Science II (B+), Linear Algebra (B-), Probability (B), Spatial Statistics (B+), Data Structures and Algorithms I (A-), Databases (A-), Theoretical Statistics (B+), Computer Systems (B+), Networks & Cryptography (A+), Data Structures and Algorithms II (B), Advanced Data Analysis (B+), Machine Learning (A-), Mobile Computing (B+). GRE: 161Q, 161 V, 4.5 Writing Research/Work Experience: Currently work as a Data Analyst at Columbia University Medical Center (a year), with having previously worked at IBM Watson Health as a Data Scientist (a year and a half). I have two research publications and have been working on multiple manuscripts and abstracts for conferences. I also participated in the Biostatistics Program for Underrepresented Students at Columbia University two summers in college. Applying to: QBS PhD Program at Dartmouth Ohio State University Drexel University George Washington University Rutgers Boston University UMASS University of Pittsburg UNC Chapel Hill Virginia Commonwealth University University of Maryland Vanderbilt University Emory University Brown University University of Pennsylvania
  16. Hey all! I'm looking for advice on where to apply, and an idea of where I'd get into for Fall 2020. I’m looking for schools that also have a PhD program in case I decide to keep going after Master's. I know my grades are not the highest and my GRE isn’t the best either, but I’m hoping I still have a chance. Thanks for your help! Undergrad Institution: Big state school Major(s): Statistics & Math Minor(s): n/a GPA: 3.758 Type of Student: Domestic white female GRE General Test: Q: 159 (70%) V: 153 (60%) W: 4.5 (81%) (Taking again soon, hoping to bring Q to 80%) GRE Subject Test in Mathematics: M: N/a TOEFL Score: N/a Programs Applying: MS in Biostatistics Research Experience: Did a Summer institute in biostatistics (SIBS) Program Awards/Honors/Recognitions: STEM Scholarship, deans list (all semesters but 1), university scholarship, math honor society, scholars program Pertinent Activities or Jobs: Math grader Letters of Recommendation: Professor for two of my stat courses (knows me well), instructor from SIBS (worked on research project together), mentor from STEM scholarship (knows me extremely well, but not in stat). Math/Statistics Grades: Calc I (AP), Calc II (B+), Vector Calc (A), Statistical Methods I and II (A, A), Linear Algebra (B+), Probability (B), Math Stat (B), Intro to Experimental Design (A), Transition to Adv. Math (A), Vector Analysis (A), Algebraic Structures (C) Currently taking: Theory of Statistical Inference, Computing in Statistics, Big Data Analytics, Analysis, and Ordinary Differential Equations (Also have 2 transfer courses in biostat from SIBS program both with A's) Any Miscellaneous Points that Might Help: Graduating a year early, almost all math and stat classes are 500-level with lots of theory Applying to Where: Duke Colorado - Denver UNC Boston (MS or MA) Emory Tulane Brown Probably some other (pls help)
  17. I am very interested in pursuing biostatistics PhD for this coming year (2020). I realize I have a low gpa score but have a strong research record/work experience to compensate. I would really appreciate honest feedback about my chances for graduate school this coming year given that I can get very strong LORs. Undergrad Institution: Top 10 LAC Major: Computer Science and Statistics GPA: 3.56 Student: Domestic POC, female Courses: Intro to Stat Modeling (A-), Intro Computer Science I (B+), Multivariable Calculus (A-), Intermediate Statistics (A-), Intro Computer Science II (B+), Linear Algebra (B-), Probability (B), Spatial Statistics (B+), Data Structures and Algorithms I (A-), Databases (A-), Theoretical Statistics (B+), Computer Systems (B+), Networks & Cryptography (A+), Data Structures and Algorithms II (B), Advanced Data Analysis (B+), Machine Learning (A-), Mobile Computing (B+). GRE: 161Q, 161 V, 4.5 Writing Research/Work Experience: Currently work as a Data Analyst at Columbia University Medical Center (a year), with having previously worked at IBM Watson Health as a Data Scientist (a year and a half). I have two research publications and have been working on multiple manuscripts and abstracts for conferences. I also participated in the Biostatistics Program for Underrepresented Students at Columbia University two summers in college. Applying to: QBS PhD Program at Dartmouth Ohio State University Drexel University George Washington University Rutgers Boston University UMASS University of Pittsburg UNC Chapel Hill Virginia Commonwealth University University of Maryland Vanderbilt University Emory University Brown University University of Pennsylvania
  18. Hi all, I am not going to ask you guys to chance me, as I know the application cycle will be an uphill battle for me from a low GPA and non-traditional background. I majored in Economics at an Ivy League with minors in Math and Statistics. I didn't do so well in the Economics with a few C's, a few A's, and mostly B's,(major GPA ~ 3.1) while my Math (mostly A's with an occasional A-), Stat, and other STEM courses such as CompSci and Econometrics was around 3.75. My cumulative GPA including the 'general ed' courses was right below 3.40, with the lowest semester being the first semester of my third year. I finish both my senior semesters with a 3.9. It seems that my GPA progression is hyperbolic and concaved upwards over the semesters. I will have taken up to Real Analysis, scoring A-/A in my math courses from undergrad and graduate institutions. Now, I am enrolled in my final year in a statistics masters program at a mid tier state school (to be specific - mid tier for statistics) and will be expecting a final GPA between 3.8-4.0. I will also be completing a master's paper on the topic comparing multivariate time series models using foreign exchange data (not a publication in a journal). My interest lies in financial engineering and multivariate statistics. My GRE is V:160/Q:166/W:4 (I plan on retaking. Also I am taking the GRE Math subject to hopefully scoring between the 50th to 70th percentile. The higher the better but without the math major, I don't know how feasible it is.) I have around 1 year of work experience in finance and data analytics (business strategy) as I recently finished my undergrad. So my questions for PhD programs are: 1) Besides the big names such as Columbia, Princeton, Cornell, and Berkeley, where else offers such programs with respect to my interest in financial engineering and high dimensional statistics? I'd like to stay on the coasts. 2) Which schools are more reasonable to be set as target schools? 3) Is it worth my while to work towards a post-bac in math to compensate for the GPA and gain the necessary coursework? Any advice would be much appreciated.
  19. I'm interested in applying to Stats and Data Science MS programs. The USNWR and AMSTAT top schools list is helpful (for stats), but I'd like to see a larger list. I've come across some interesting programs just by looking up x school (not on the best ranked lists) and looking up programs they're offering. Does anyone know of any resources that post a list of all data science programs and/or statistics programs in the United States? This resource from CSU East Bay is kinda what I'm looking for, but it's dated. Looking up data science programs is trickier-- no USNWR or AMSTAT lists, but instead a lot of "20 Best Value Schools for Data Science Masters" type results. A little bit off-topic, but should I just stray away from considering schools that don't score highly on USNWR (or are even unranked)? It seems like a ton (most?) of the posters on GradCafe are only seriously considering Ivy-League or similar status schools. I'm sure the elite schools consistently beat out lower ranked schools across most metrics, but is it to the point where I shouldn't even consider the lower ranked?
  20. Hi all, I'm deciding between Columbia's Masters of Science in Business Analytics and University of Michigan's Masters of Science in Data Science program. Since they are in different industries, I'm very conflicted. I got waitlisted from NYU and UW Data Science, got accepted to Cornell's MPS in Applied Statistics (Data Science), ORIE at Cornell Tech, and Georgetown Analytics. Still waiting from Brown, PENN, LSE Data Science. Economics major and Statistics minor at a top 3 liberal arts college, with some cs background. I think my end goal is working as a data scientist at a consulting/finance firm, but I'm open to other data science roles. Not interested in PhD. I was leaning towards Michigan because of technical complexity, so I'll have a wide variety of career options, but everyone's telling me to choose Columbia because of its name value, resources, and geographical advantage (i.e. recruiting and networking). I have a week to decide - any advice/input would be appreciated!!! Thank you!
  21. Hey everyone, I will be applying to Statistics PhD programs for fall of 2020. I am mostly interested in probability theory and general statistical theory. Any advice is greatly appreciated! Undergraduate: Small public university. Relatively small math department. Major: Mathematics GPA: 4.0 Type of Student: White Male Relevant Courses: Calculus I, II, and III, Ordinary Differential Equations, Linear Algebra, Abstract Algebra, Modern Algebra, Discrete Mathematics, Advanced Calculus I (Real analysis), Numerical Analysis I, Financial Mathematics, Life Contingencies, Intro to Statistical Methods, Applied Reg/Time Series, Nonparametric statistics, Statistical Process Control, Mathematical Statistics I and II, Foundations of Computer Science, Fundamentals of Programming, Object Oriented Programming, Intro to Algorithms and Data Structures (A's in all courses) GRE General Test: Q: 168 V: 160 W: (waiting on score) Research Experience: Statistical consultant on a medical paper currently in peer review, not expected to be officially published before application. Additionally, I worked with a professor in mathematical research. I was primarily in charge of the computer programming to simulate our enumeration problem; research stopped due to professors family crisis. Awards/Honors/Recognitions: Valedictorian of the College of Science, Outstanding Math Student Award, Dean's list each semester. Letters of Recommendation: Professor (Department Chair) I worked with closely as a TA and took courses from, Professor I took classes from, Assistant Professor I took classes from. Additional Experience: Experience working in R, SQL, Python, C++, C#, and LaTex. I have taken three actuary exams and pass all three. I have a year's experience working as an actuary. A few internships during the summers of my undergraduate career in Cyber and Actuarial Science. I have ample experience in math and statistics TAing and tutoring. Applying to: Texas A&M, Colorado State University, University of Iowa, UC Davis, Virginia Tech, (Other schools suggested?) Comments/Questions: I'm curious to know if I'm aiming for the right caliber of schools. I am concerned about not having published, how will this effect my application?
  22. I'm honestly just curious to see if you think that with my credentials I could get into any PhD program. I'm at an odd point in my career--the original plan was to go for an Econ PhD, but I realized pretty quickly into my current job that I'm not interested enough in Economics for that. I still want to learn enough math to be able to answer interesting questions about the world, though. My current plan is to enter a statistics masters program with the hope to later apply for PhD spots, though if it's possible to just enter a statistics MS/PhD program now that would be great to know. I am NOT looking into schools in the top 20-30--I have somewhat realistic expectations, and also for me what matters more is location/what the professors specialize in. My interests lean towards environmental or applied statistics. Undergrad: Top 50 Public University Majors: Economics and Statistics GPA: 3.62 Student Type: Domestic, White Female Math Courses: Calc 1 (B+), Calc 2 (A-), Calc 3 (B+), Differential Equations (A), Probability Theory (A), Math Stats (B), C++ Programming (A), Linear Algebra (B+), Stochastic Modelling (B+), Applied Regression Analysis (B), Time Series Analysis (A-) GRE General: Haven't taken it yet, but I anticipate very good writing/reading scores and ~163 quant. Math GRE: NA Research: Undergraduate research for an Econ professor. I'm in my second year of a 2-3 year Research Assistant position at a government agency. I work daily with PhD economists. Most of my work is more data management/low-level statistics; I'm essentially on R playing with huge datasets all day. I am not on track to get my name on a paper during my time here, though I am involved in multiple projects. The work I do is not related to the field I want to go into (it's basically all finance-related). Letters of Recommendation: One will probably be an Econ professor I was close to in undergrad. Another will be my chief, who I've worked with on small projects and have a great relationship with. A final one will be the economist I do most the most research with. Concerns: My math grades are not great due to a mix of me being lazy/thinking that Econ was the most important thing to focus on in undergrad. I haven't taken Real Analysis, though I am doing so this Fall and intend to work harder in it than I did in my undergrad classes. I don't have any actual research papers to my name. Also, I don't have a clear vision for higher education (e.g. "I want to study the migration patterns of X animal since 2005 to prove that...") so I feel like I'd come off as someone who has no idea what they're doing.
  23. Hi! I'm a recent graduate planning on applying to PhD programs in statistics or data science for Fall 2020. My research interest lies in the field of Natural Language Processing. Undergraduate Institution: Western-style university in Central Asia (Nazarbayev University) Majors: Mathematics, minor in Economics GPA: 3.57/4.0 (lower in the first year when my major was Chemical Engineering, and higher after I transferred to Math) Type of Student: White Female Relevant Courses: Math & Statistics: Calculus 1, 2, 3 (A, A, A) Linear Algebra (A) Probability (B) Applied Statistical Methods (A) Math Statistics (B) Regression Analysis (A-) Design of Experiments (A-) Intro to Proofs (B+) Real Analysis (B+) Nonlinear Optimization (A) Actuarial Math (A-) Computer Science: Programming for Scientists and Engineers (A) Performance and Data Structures (B+) Statistical Programming (A) Other: Econometrics (A) Economics of Financial Markets (B) Capstone Project in Math (A) GRE General: Q 167 (91%) , V 152 (56%), AW 3.5 (41%) Research Experience: My research was generally focused on the development of computationally efficient and interpretable algorithms for obtaining word embeddings and sentence embeddings. First publication: Springer "Lecture Notes in Computer Science", topic: NLP (word embeddings), first author, conference: CICLing 2019, top 10% of papers Second paper: topic: NLP (sentence embeddings), submitted to AAAI 2020 Research internship in South Korea (UNIST), topic: networks theory In plans: research internship at KAUST 1,5 years experience of working as RA Work Experience: Currently working as a Data Scientist in a Venture company Letters of Recommendation: One from the research advisor (I also took 3 classes from him) One from an academic advisor (He is currently working at the University of Minnesota) One from a professor of Nonlinear Optimization (previously he worked at University of British Columbia) Concerns: This year I was accepted to NYU MS in Data Science and UIUC MS in Statistics, but to the several financial circumstances, I was not able to join any for Fall 2019. That is why I changed my plan, and now I want to apply to the PhD program which could possibly provide the assistantship. I want to continue my research in the field of NLP, but after a search, I realized that most of the professors interested in NLP are working in CS departments (or even in linguistics), but my background is mostly related to Statistics. My dream university is NYU (Courant), but I am not sure about my chances. I will be happy to read your advice and grad schools suggestions. Thank you very much!
  24. Hey everyone here, I'll be applying to various universities this December onwards, I'll graduate next year in May. I plan to pursue a Masters degree in Statistics, and would realistically like to know my chances at one of the top 10 universities (ETH Zurich claiming top spot). Thanks! Here's my profile: Undergrad: IIT Guwahati (India) - Mechanical Engineering with a minor in Computer Science - CGPA 8.39/10 Profile: Indian, Male, 20y TOEFL: 116/120; GRE (expected): 170Q 162V 4W Relevant coursework: I have attached my course structure for MA101,102, 201 as well as the CS courses down in the attachments section since I didn't know how to filter stuff out. Here are the minor course (CSE) contents: Functions, relations, partial orders, recurrences, summations, generating functions, asymptotics Graphs: basic concepts Elementary Logic and proof techniques. Alphabets, Languages, Grammars Finite automata: regular expressions, regular languages Context free languages: pushdown automata Turing machines: recursively enumerable languages Chomsky hierarchy. Review of fundamental Data Structures Models of Computation: random access machines, space and time complexity measures, lower and upper bounds Design techniques: the greedy method, divide-and-conquer, dynamic programming, backtracking Sorting and Searching Graph algorithms Hashing: separate chaining, linear probing, quadratic probing Search Trees: binary search trees, AVL trees, B-trees NP-completeness. Boolean Algebra Minimisation and realisation of switching circuits Basic building blocks of combinational circuits: Multiplexer, De-multiplexer, Encoder, Decoder, Adder, Subtracter Design of synchronous sequential circuits: Flip-flops, Registers, Counters, Finite State Machines, State tables and diagrams, Excitation functions of memory elements.Instruction sets with various addressing modes Memory organisation: ROM, Cache, Main Memory CPU design: ALU, Control unit design: hardwired and microprogrammed I/O transfer: Program controlled, Interrupt controlled and DMA. Introduction to structure and organization of computer systems, operating systems, and networks Processes and threads and their scheduling, synchronization, deadlocks in concurrent processes Memory management basics, demand paging and virtual memory implementation File system design and implementation.Basics of digital communication, digital transmission of data, modulation Multiplexing Data link control with sliding window protocols, error control Local area networks, Ethernet, wireless networks Concepts of switched networks Internet addressing and routing algorithms Transport protocols, UDP, TCP, flow control, congestion control Application layer protocols such as DNS, SSL, Web. Introduction: software engineering principles, life cycle Requirement specification: styles, operational and descriptive Design: a brief concept on objects, data abstraction,inheritance, polymorphism, data encapsulation, software design using functional andobject oriented approaches, architectural, component-level and user interface design Brief introduction on database system (specially SQL, MySQL) Verification: testing, validation Software reuse: design patterns Software management Software Modeling: UML. Research experience: None, haven't released a paper in my undergraduate studies. This might be the weakest point on my profile along with the subpar CGPA (I think). Letters of recommendation: I'll probably get good letters from my BTech dissertation supervisor (reinforcement learning) and I have a good one from a company I interned at this summer (neural networks). Other than that I'll be able to get one from the Mech Dept professors. What do you guys think? Please let me know if I haven't mentioned anything important, I tried being as clear and open about it as possible. Thank you! ❤️ CS101.pdf CS110.pdf MA101.pdf MA102.pdf MA201.pdf
  25. I'm curious about some Canadian universities for their statistics programs, but I've heard numerous times that the way master's programs work in Canada is much different than the ones in the US. A few questions I had was: 1. Generally, are Canadian programs usually funded and geared towards research careers? I'm personally not looking for a research career. 2. As an American, do I have to apply with a TOEFL? 3. Am I ineligible for any kind of Canadian federal/provincial funding or aid as an American? Can I take out loans in the US and use them toward Canadian programs? 4. Most statistics programs in the US consist of international students. Is it similar in Canada? And is it much harder to get in as an American (or international) student to master's program in Canada? Any other thoughts or comments are welcome. Thanks in advance for all the advice!!!
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