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  1. 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
  2. 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!
  3. 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!
  4. 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 !
  5. 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!
  6. 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
  7. 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)
  8. 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
  9. 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.
  10. 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?
  11. 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!
  12. 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?
  13. 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.
  14. 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!
  15. 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
  16. 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!!!
  17. Hi all, I'm a senior undergrad in the US graduating in the spring. I'm applying to PhD programs in statistics/biostatistics for fall 2020. I'm wondering about several things: 1. Are programs in biostats vs statistics significantly different? I think I'm interested in a more theoretical/mathematical training so if biostatistics programs put a lot less emphasis on that then I'm not sure I'd want to attend those programs. 2. Are the schools I'm applying for reasonable? Suggestions on where else to apply would also be great. 3. Will not taking the math GRE subject test significantly weaken my application to higher ranked schools? I'm not sure if putting in the time to prepare and take it is worth it; I was planning on taking a practice test and seeing how that goes. 4. Should I retake the general GRE? I think I could get at least a 4.5 writing score and a higher quantitative score with not that much more preparation. Undergrad Institution: Large public university ranked 15-20 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: (not sure if I will take) Programs Applying: Biostatistics or Statistics PhD 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 the statistics prof I think 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. In general I didn't form that close of relationships with my professors so I'm sort of worried about this last letter but I have several professors I took classes with as backups. Grades: Mathematics: Calc III: B Linear Algebra and Differential Equations(proof based): A Multi-Variable Calculus(proof based): A "Applied Mathematical Analysis" which was a mixture of harder calc III and some complex analysis: 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 Computer Science: Intro. to programming: A Data structures: A Intro. to Combinatorics: A Physics: I took 3 intro classes, a lab course, quantum mechanics, thermodynamics, classical mechanics and electrodynamics and got all As. Initially I thought I wanted to do a physics PhD which is why I took so many physics courses and not so many statistics courses. Additional courses I will have taken before applying: Numerical Analysis Stochastic Processes Schools I'm thinking about applying to(and US news ranking): John Hopkins biostats 3 University of Chicago stats 6 University of Michigan Ann Arbor 12 Duke stats 12 Columbia stats 16 University of Wisconsin Madison stats 16 Iowa State University stats 20 Minnesota twin cities stats 24 Ohio State University 37 University of Illinois Urbana-Champaign stats 37 Any suggestions/feedback would be great! Thanks
  18. 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.
  19. Hi everyone! I am currently in one of the top universities in China and will enter my junior year in September. I am posting this for seeking some advice on course selection. Since I am going to apply for Statistics/Biostatistics PhD program, I have arranged my course schedule last two years for preparation. Here are some courses and grades related to application. Mathematical analysis 123 (A,A,A-) (FYI, in China almost every school combines calculus and analysis together, so these courses can be viewed as “calculus+intro to analysis”.) Linear Algebra 12(C+, Probability based on calculus (A-) Mathematical statistics (A,definitely ace it, rank 1/75) Numerical analysis(A-) Real analysis(A) (The content includes basic topology, Lebesgue measure and integration, some introductions to Lp space like Holder inequality) I plan to take following courses next semester but there are still some options to make. Nonparametric statistics(proof-based) Topology Applied stochastic process PDE Here are some questions: 1. There is still one option between applied regression and functional analysis. In order to achieve the goal to a decent stat/biostat PhD program, which course is better? 2. Because of the personal reason, the Linear Algebra 1 I took in the first semester of freshman year definitely damaged my profile. So if I have chances to take an advanced algebra course that will cover all the content of it and also include more advanced topic like QR decomposition and SVD, is it a good idea to take it? 3. Do I need to take measure theory? Any other courses that can be helpful for the application and future research? Any advice will be much appreciated.😊
  20. Hi, I'm going to apply to Statistics master's and PhD program this year, hopefully focusing on machine learning / deep learning. I have several concerns, including 1. changing my field from Economics to Statistics 2. did not take many courses from Statistics department 3. did not major/minor in Mathematics, and for these reasons, I am applying to master's as well, so that I can have a better chance to get into PhD program afterwards. Any feedback would be greatly appreciated! Undergrad Institution: One of the top 3 in South Korea Major(s): Economics GPA: 3.95/4.00, graduated with Magna Cum Laude Exchange Student Program: UCLA (undergraduate, one year during junior) Major(s): Mathematics GPA: 3.79/4.00 (Math upper division GPA: 3.89/4.00) Graduate Institution: The same university as undergrad (One of the top 3 in South Korea) Major(s): Economics GPA: 3.90/4.00 Type of Student: International Asian Male GRE General Test: Q: 154 (65%) (trying to retake to improve the verbal score this summer) V: 170 (96%) W: 4.0 (59%) Subject(Mathematics): 930(99%) TOEFL: 110/120 (S 25, W 27) Courses taken: Mathematics: Calculus (A+), Calculus with Vector Analysis 1 (A+), Calculus with Vector Analysis 2 (A+), Linear Algebra (A+), Math for Econ 1 (Linear algebra/Calculus, A+), Math for Econ 2 (Optimization/ODE, A+), Number Theory (A+), Probability Theory A (A+), Probability Theory B (A), Stochastic Process (A-), Real Analysis A Honors (A), Real Analysis B Honors (A--), Topology (A-), Scientific Computing (A+), Numerical Analysis (P), Optimization (A+), Mathematical Game Theory (A+), Real Analysis A (Graduate, B), Real Analysis 1 (Graduate, A-) Statistics: Intro to Stat (A+), Statistical Methods (A+), Computer Programming (A+), Econometrics 1 (from Econ department, A), Mathematical Statistics 2 (A+), Mathematical Statistics (Graduate course from Econ dept., A+), Statistical Learning Theory (Graduate, A+) CS/EE: Software Programming (A+), Data Structure and Algorithm (A), Discrete Mathematics (A), Artificial Intelligence (A+) Courses will take this fall (all graduate courses): 1. Real Analysis B (for sure), 2. Artificial Intelligence Theory (possibly), 3. Searching and Text mining (if not take 2) Research Experience: One research on applied Econometrics project (irrelevant to Statistics) / currently working on bridge regression Working Experience: 3 years experience of TA (Math for Econ, Econometrics, Mathematical Statistics (graduate)) Awards/Honors/Recognitions: Honors for every semester in my home university and one Dean's list from UCLA, several merits-based scholarship Pertinent Activities or Jobs: Statistics club (one year), data analysis assistant for the university freshmen evaluation team Letters of Recommendation: One Econometrics professor (with whom I did research), one math professor from UCLA, and one Statistics professor. The first two will presumably be strong, and I am not sure for the last one. Any Miscellaneous Points that Might Help: 1. I got B on graduate Real Analysis A (measure theory) from UCLA when I was junior. I believe I was not mathematically-matured enough at that time, and I did kind of retake the course in my home university and got A-. And I am going to take Real Analysis 2 this fall to complement B on measure theory, even though I am not sure if it can fully compensate B on the measure theory. 2. I got P on Numerical Analysis just because it was my undergraduate complement course and I was not allowed to take it with letter grade. The class rank was 1/37. 3. Even though I am currently in Economics department as an graduate student, I have not been taking Econ courses for a while. And I am not getting MA in Econ here. I just happened to have a chance to take three semesters without tuition in Econ graduate program in my home univ., and I was complementing math and CS/EE courses during the this period. 4. I actually applied to 8~9 Statistics Master/PhD programs last year and got all rejected. Most of them were top schools like Stanford MS or CMU PhD, but it also included non-top schools in Statistics like UCLA MS. I had pretty similar profile except that my GRE subject math was 830 (83%) at that time, which I submitted to all of the schools I applied. Program: PhD/Master in Statistics (and possibly OR MS) School List: Master's in Statistics: UC Berkeley, U of Chicago, U of Washington, U of Michigan, UCLA, UCSD PhD in Statistics: NCSU, U of Wisconsin, ISU, PSU, Purdue, UCD, Florida State U, UC irvine, UCSB I don't know which schools are my reach/match/safety. I am also thinking to apply to Georgia Tech OR Master's and USC CS Master's if it fits my profile better, since OR and CS also work on machine learning / deep learning. Thank you for your time and advice!!
  21. Hi all, I was thinking about graduate school, but I'm not sure where I could get in given my grades. I would like to apply to Statistics PhD programs. I'm planning to just take the standard GRE. Undergraduate Institution: Columbia University Major: Mathematics-Statistics GPA: 3.88 Type of Student: Domestic Asian Male Relevant Classes: Statistics Courses: Statistical Inference (Casella and Berger) (A) , Bayesian Statistics (A), Statistical Machine Learning (A-), Stochastic Processes (A), Probability Theory (A), Math Courses: Calc III-IV (A), Linear Algebra (A), Optimization (A), Algebra I (B+), Analysis I-II (A), Probability Theory (measure theoretic) (A-), Fourier Analysis (B), Numerical Methods/Analysis (A+), ODE (A) MISC: Data Structure and Algorithms (A) GRE: Haven't taken Research Experience: Got accepted to a biocomputing conference and gave an oral presentation (Bayesian classification). Currently working with a professor on machine learning projects applied to neuroscience. Recs: They should be standard. I'm mainly worried about my B+, and B in Algebra and Fourier Analysis, (and a little about my A- in Probability Theory). I feel like my math background is still rather light and those grades look rather bad. I'd really appreciate comments.
  22. Hi all, With schools starting to open up their applications in the next 2-3 months, I've been wondering what schools I should aiming for. I want to get into healthcare using math/stats so most of the programs I was considering are bio-statistics or operations research with a focus on health systems. I have a pretty good academic profile but some concerns I have are that 1) my undergrad has zero prestige; 2) my GRE score seems a little low for the top programs; 3) I have no direct healthcare experience (currently work in unrelated economic research). Below are my stats but I'm not sure how competitive my profile would be. Obviously, I would love to get into top schools like Harvard Biostats or Stanford MS&E, but I have no idea whether my GPA/GRE is really enough. Given my profile below, what type of schools should I be aiming for? Am I being delusional for thinking that I should even apply to a place like Harvard? What "tiers" should I be aiming for? Any help would be greatly appreciated. Thanks so much!!! Undergraduate Institution: CUNY - Hunter College Major(s): Math GPA: 4.00/4.00 Type of Student: Domestic Asian Male GRE General: 166Q (90%); 164V (94%); 4.5 AWA (82%) Programs Applying: MS Biostatistics, Statistics, and Operations Research (health systems focus) Letters of Recommendation: 1 from my mathematical statistics class professor; 1 from my epidemiology class; don't know my third letter writer yet. Relevant Coursework: Calculus I, II, III; Linear Algebra; Ordinary Differential Equations; Vector Calculus; Discrete math; Real Analysis I ; Complex Analysis; Abstract Algebra I ; Stochastic Processes; Numerical Methods & Analysis; Mathematical Statistics; Intro to Probability Theory; Intro to Epidemiology Skills: R, Stata, Python, SQL (I have an active Github portfolio with all my code) Relevant Research: None, but I'm not aiming for a PhD Work Experience: Software Engineer for 2.5 years, and now currently doing data analysis doing economics research
  23. Hello, everyone! I'm a rising senior thay has just recently made the decision to pursue a MS in biostatistics rather than the original path I've been working at for the past couple years (PhD in Clinical Psychology). Clearly, this is a major shift in my plans so I have been attempting to familiarize myself with the field and what credentials I'll need in order to be admitted into such a program. The underlying question of this post: do I even have a chance of admission for Fall 2019 or I should apply for the following year? Undergraduate Institution: Large Public University (top 60 in national universities, top 20 in public universities) Major(s): Psychological Sciences, BS GPA: 3.92/4.0 (I had an "off" semester) Type of Student: Domestic URM Female GRE General Test: (TBD, expecting 165 Q and V) Programs Applying: MS Biostatistics Research Experience: 2 years as a research assistant for five laboratories in different areas of psychology (not all at once, currently in two), 1 year as a project manager for a large meta-analysis in clinical psychology, 1/2 year developing a senior's thesis pertaining to pathological personality under the guidance of a mentor as part of a research-focused honors program for a small cohort of PSY students (program will last until graduation, culminating in a potential publication and poster presentations), just started in a lab this summer in the area of quantitative methods lab and clinical psychology (I'm their only research assistant so I'm getting the opportunity for meaningful involvement in projects involving advanced quantitative methods) Activities or Jobs: (N/A for work in statistics but to give more background) 1 year as a mental health technician for a group home for adults with severe mental illness, 1 year as president of mental health advocacy organization, 1 year as crisis counselor for Crisis Text Line Letters of Recommendation: research mentor (strong), supervisor for meta-analysis (strong), quantiative methods lab supervisor (strong) Relevant Coursework: Psychology - Introduction to Statistics in Psychology, Research Methods in Psychology, Understanding and Analyzing Experiments (ANOVA and Research Design), Introduction to Bayesian Statistics, Honors Research Seminar I (all As) Math & Statistics: Calculus I (AP credit) Honors and Awards: Phi Beta Kappa, Office of Undergraduate Research Scholar ($1000 scholarship program), Psychological Sciences Research Focused Honors Student Skills: programming in R (developing skill under the guidance of the quantitative methods lab I'm in, hope to become proficient) Plan of Action: Fall - Calculus II Spring - Calculus III, Linear Algebra, Statistics Summer (considering) - Real Analysis, ODE, Probability Questions: 1. Unfortunately, I'm currently in a position of playing catch up on prerequisite coursework. I have a plan to take the classes I need, but my grades will of course not be finalized in time for the many programs that have application deadlines in early December. My concern is that the programs will have nothing to go on as far as my ability to take rigorous math courses and will thus not be able to seriously consider me for admission. For the programs that have early spring deadlines, they will only be able to see my grade in Calculus II. This is the primary reason I'm asking if I even have a chance of admission. Thoughts? 2. If I do have a chance, which range of programs should I be applying to? My worry is that many programs I've been looking into are out of reach given my background. 3. If I likely don't have a chance of admission, what kind of jobs can I pursue in a gap year that will be benificial on my application for the following year? 4. I'm truly trying to set myself up for success this next year, but I also have a lot on my plate given commitments I made prior to this change in post-graduation plans. From what I've gathered, grades and letters of recommendation are the strongest selling points on an application. Therefore, I've been thinking of stepping down from the research focused honors program in order to give myself the chance to study more for the math classes I need to excel in (I would have more time for studying without the significant amount of credit hours from research seminars, individual study, and meetings that will take up my schedule in the fall and spring). My concern is that I would me removing perhaps one of the few pulls, if there even are any, to my application. Is this experience even something admission committees would look at favorably to warrant the added stress? I don't want to stretch myself to thin and not perform well in my courses. I know this was long so I'm grateful for you sticking with me. Any and all feedback is appreciated. Thank you!
  24. Hello everyone, I transferred from one mediocre college in China to Cornell last year and switched my major from Biology into Statistics. I am now a rising senior and not sure about if my profile is strong enough to apply for PhD/MA programs in Biostatistics and /or Statistics in 2020 fall. I didn't know before math capacity is one of the most important factors to be considered in application so the math courses I took was very limited. My advisor gave me some positive feedback and encouraged me to apply for some statistics PhD programs but I am still worried about my background. Undergraduate Institution: China Agricultural University (transferred) ----> Cornell Majors: Biological Sciences (previously) ----> Biometry and Statistics (Now) GPA: 3.84/4.00 (China) --> 3.92/4.30 (Cornell, it is also my major GPA) Type of Student: International (Asian female) Courses taken: In China: (doing good in Bio/Chem but not prominent in my math grades) Stat: Probability Theory and Mathematical Statistics(A-), Linear Algebra (A-) Math: Advanced Math A-I (A-, equivalent to Calculus I and II), Advanced Math A-II (B+, equivalent to Multi-variable Calculus and Differential Equation) CS: Intro to Information and Computational Thinking (Using Python, A-) At Cornell: Stat: Probability Model and Inference (A+), Biological Statistics (A), Linear Model with Matrices (A+, graduate level), Theory of Statistics (A+), Categorical Data Analysis (B+), Statistical Computing (A+) Math: Intro to Real Analysis (A), Numerical Analysis: Solving Linear and Non-linear System (B) CS: Object-Oriented Programming and Data Structure (Using Java, A-) Courses will take this fall: Math: Measure theory (graduate level), Combinatorics, Linear Algebra (upper division, proof-based) CS: Machine Learning for Intelligent System, Data Structure and Functional Programming GRE General Test: Q: 169 ; V: 159 (taken 5 days ago, W scores not released yet) GRE Subject Math: will take this September Research Experience: Noise Reduction for Experimental Time-domain Signals (September 2018-Present, National Biomedical Center for Advanced ESR Technology at Cornell); Machine learning in Brain-Computer Interface and Cybersecurity (January-March 2019, A 7-week research authorized by a professor at Berkeley) Quantile Regression Analysis for High Dimensional Data: Right-to-Carry Laws and Violent Crime (this summer, supervised by a well-known professor at Department of Statistics at Cornell) Working Experience: Data Analyst Intern at China Asset Management co. LTD (June 2018) Awards: First-class for Academic Excellence (in China), First Prize for Data Castle (a nationwide machine learning competition in China), One year of Dean's List (Cornell) Letters of Recommendation: Two from the professors who supervised my research, one from my academic advisor (also get 2 A+ in his classes) School List: Berkeley Biostats MA (the one with funding) is my dream program. I heard it is more relevant to data science rather than traditional biostatistics but is as competitive as PhD program. Some PhD programs I am considering would be: UCLA biostats, UC-Davis stats, UCSD biostats (I like California), Emory Biostats, Cornell Stats My advisor also encourges me to apply for some good PhD biostats program at JHU, UNC, NCSU, Duke, Texas A&M, UW and Umich (I think it is very tough based on my current profile). I haven't decided yet if I should apply for those programs in 20 Fall or 21 Fall. I know if I apply in 2021, I can take more math courses, even at PhD levels, and do more non-trivial research. But I am nor sure if it is worthwhile to wait for one more year. I cannot make up my mind. Thank you in advance for your time and advice!
  25. Based off my undergrad major (Environmental Science), I'm somewhat of a non-conventional applicant. I'd like to apply to stats MS programs this fall, but I'm going to lack some of the core pre-reqs needed for application. I'm missing Calc 3 and Linear Algebra, which I've found most programs to ask for in their minimum requirements. I plan to take Linear Algebra this fall, but probably won't be able to take Calc 3 before apps are due. @penguinbombs suggested early this year that larger programs may still be open to a non-conventional applicant if they have a strength in a field relevant to what they may do in grad school. I've been digging around various programs and have been looking close at those with Environmental Statistics disciplines. I was wondering if anyone has suggestions for schools/programs to check out for Environmental Statistics and/or programs that would be more open to taking on someone with my background? Undergrad Institution: Small state school Major: Environmental Science Minor: Statistics GPA: 3.80 Relevant Courses: Calc 1, Calc 2, Linear models, experimental design, nonparametric stats Additional notes: Plan to take Linear Algebra and Calc 3 at a school I haven't decided at yet-- either a community college, a large recognized state school, or a top 15 public university GRE: not taken yet Research Experiences: 1 summer at a recognized R1 lab, handful of side/class projects I'd appreciate any advice. Thank you.
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