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Found 296 results

  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. 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.
  3. 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
  4. 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!!!
  5. 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
  6. 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.
  7. 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.😊
  8. 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!!
  9. 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.
  10. 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
  11. 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!
  12. 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!
  13. 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!
  14. 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.
  15. Undergrad Institution: big midwest state school ( ranked <30 on statistics) Major(s): math and stat double GPA: 3.83 Type of Student: international female Grad Institution: not good private school (ranked 50-70 on stat US News) Concentration: statistics MS GPA: 4.0 GRE General Test: Q: 170 V: 151 (52%) W: 3.5 (<50%) GRE Subject Test in Mathematics: M: took two years ago, did really bad TOEFL Score: N/A Programs Applying: Statistics and Biostatistics PhD Research Experience: Summer data analysis in a biology lab, haven't done much, only used basic lm and glm. Another biostat related machine learning summer research in an ivy school, may publish paper, not sure if it can be first author Awards/Honors/Recognitions: 2 years Dean's list for undergrad, tuition fellowship for MS program Pertinent Activities or Jobs: tutored for a year, TAed for a year. Both are for introductory stat undergraduate classes. Letters of Recommendation: 1 my advisor for my master program, took his class and got an A. 2 chair of the department, took his class and got an A. 3 research supervisor from the ivy school Math/Statistics Grades: Have taken a good amount of undergraduate math courses, got mostly A's, only 2 B+. Have taken master level probability and statistics (Casella & Berger) (A). Also took some applied statistics courses on both undergrad and grad levels. Any Miscellaneous Points that Might Help: currently in a stat master program, and plan to take two PhD level probability and inference in Fall 2019 I want to apply to biostat phd program in Columbia, UCLA, Penn, Yale, Brown, BU, Duke, UNC and UMN, and stat PhD in UCLA , JHU, NCSU and UC Irvine. But I have no idea if the goal is too high for me? Which should be reasonable schools to apply? Also, my GRE score is one of my biggest concerns. Should I take GRE again to improve my verbal and writing? I don't know if GRE score is that important...
  16. I'm looking for advice on what tier of school I'd be competitive for. Any input would be highly appreciated. Undergrad: The Ohio State University Majors: Mathematics/Economics Ethnicity: South Asian Overall GPA: 3.92 Math GPA: 3.79 Econ GPA: 4 Courses: (including all the math and only some economics) Except the first two calculus classes and the economics classes, all are proof based and honors level. Single variable calculus (A) Multivariable calculus (A) Intro to Proofs (A) Real Analysis I (A-) -- single variable real analysis Linear Algebra and Ordinary Differential Equations (B) Real Analysis II (A) -- metric topology and multivariable analysis Applied Algebraic Topology (A) Combinatorics (A-) Probability (A) -- measure theoretic Complex Analysis (A) Econometrics I (A) Econometrics II (A) GRE -- Haven't taken it yet Research Experience: None Other highlights: Am doing reading course type option with two statistics professors (e.g. reading papers and meeting weekly to discuss for this summer. This fall I'll be taking the first of a 2 course sequence in abstract algebra along with a mathematical statistics course. I'm not too worried about either. Interests: Still unsure, but I'm interesting in more theoretical programs than applied ones. Type of Program: Statistics PhD programs My main weakness is probably going to be limited research, but I'm hoping that I will have good letters to compensate.
  17. Undergrad Institution: Private liberal arts university in Texas Major(s): Biology GPA: 3.37 Type of Student: Domestic, URM, Female GRE General Test: Have not taken yet Programs Applying: Masters in Statistics or Biostatistics Research Experience: Led my own research study on vertebrate reproductive physiology, never published but data collected has been used on several published papers. Awards/Honors/Recognitions: 3 grants awarded from three different academic societies, presented at 11 conferences (2 awards), published in an undergraduate research journal (JSOURCE). Pertinent Activities or Jobs: Worked in a hospital pathology lab for 3 years after graduation because I thought I wanted to be a physician (hated it) and am now working in accounting and business. Do some light analysis using Power BI and excel for company. Letters of Recommendation: Going to ask my former Statistics prof who is also the dean of department, my former research adviser and one of the pathologists I worked with at hospital. Math/Statistics Grades: Calculus (B) Stats (B+). Plan on taking remaining pre-reqs. Have already signed up for Calc II and intro to programming. Any Miscellaneous Points that Might Help: Do a lot of volunteering. Am teaching my self to code leading up to applying. Concerns!- I am new to this field and want to know if I am a good candidate (since my GPA is not awesome) before I totally invest myself in taking all the remaining math prerequisites I need to apply. I have not idea where to find good applicant acceptance stats and have no idea what schools would be a good fit for me. Also, will taking the following courses be sufficient to apply (Calc II, Calc III, linear algebra and intro to programming)? I took a lot of chemistry and physics in college so I think I have a good math foundation. If I transition into working as a business analyst will this be helpful/ look good on application?
  18. At the end of March I took the GRE and got the above scores. I know I could have done better on the Quantitative and am on the fence about taking it again. Just curious if there are any success stories with a Q score this low for a stats program. Other background info: Mathematics Major w/ Stats Minor - 3.49 GPA 3 Research Projects, 2 conference presentations (JMM & IFORS) Currently a data analyst for an adtech company working with control/exposure experiments and homing internet connected devices together I had very good relationships with my professors and they have written me glowing letters in the past I am currently looking at some schools I am interested in and their avg/required scores; however, a lot of schools do not have them posted. I would hate for my low quant score to hold me back in the application process. Any advice would be awesome! Thanks!!
  19. I took the GRE on October 30 and received the following: 157V, 162Q, 5.5 AWA. I'm trying to get into a decent master's program for applied statistics/quantitative methods. I'm completely fine with what I scored on verbal and writing, but my quant score was dramatically lowered than what I both hoped for and expected. On most practice tests, I was scoring around 165/166Q. Is it worth retaking when there's little chance I will improve my verbal and AWA scores, just quant?
  20. Past years: 2013, 2014, 2015, 2016, 2017, 2018 Here's the thread to submit your profile and results for stat and biostat programs for Fall 2019. You only have an hour after you post to edit, so it is best to post only when you have all of your results or have made a decision. Give as much detail as you feel comfortable with! Below is the template: Undergrad Institution: (School or type of school (such as Big state/Lib Arts/Ivy/Technical/Foreign (Country?)) Major(s): Minor(s): GPA: Type of Student: (Domestic/International (Country?), Male/Female?, Minority?) GRE General Test: Q: xxx (xx%) V: xxx (xx%) W: x.x (xx%) GRE Subject Test in Mathematics: M: xxx (xx%) TOEFL Score: (xx = Rxx/Lxx/Sxx/Wxx) (if applicable) Grad Institution: (school or type of school?) (if applicable) Concentration: GPA: Programs Applying: (Statistics/Operation Research/Biostatistics/Financial Math/etc.) Research Experience: (At your school or elsewhere? What field? How much time? Any publications or conference talks etc...) Awards/Honors/Recognitions: (Within your school or outside?) Pertinent Activities or Jobs: (Such as tutor, TA, etc...) Letters of Recommendation: (what kinds of professors? "well-known" in field? etc.) Math/Statistics Grades: (calculus sequence, mathematical statistics, probability, real analysis etc.) Any Miscellaneous Points that Might Help: (Such as connections, grad classes, etc...) Applying to Where: (Color use here is welcome) School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined
  21. I am a sophomore in UT Austin. Our university does not have statistics degree for undergrads so I am here for help :( I am interested in applying for statistics grad school in the future. Any courses may help me solid the base? My enroll date is April 30th, plz help me asap ) I've attached the courses here, for any more descriptions, please click these links below :) https://www.ma.utexas.edu/academics/courses/course-descriptions#upper-division-courses https://stat.utexas.edu/undergraduate/courses-undergraduate
  22. Hello, it is only a couple of days until the April 15 deadline and I just came off of Princeton's ORFE phd waitlist. I had been pretty much all set to go to UW Stats PhD program, but I just wanted to know what the community's opinions are between Princeton's ORFE program vs. UW Stats program. These are both fully funded programs with UW giving extra fellowships on top. Specifically, my interests lie in mathematical connections to statistics. My goal is to do research in a Statistics/Math department in the future. Given this, what are your thoughts? Any help/thoughts would be super appreciated. I'm really kind of freaking out D=
  23. Hello guys, I was fortunate to receive offers from Johns Hopkins PhD in Biostatistics and Columbia PhD in Statistics. May I have some sincere opinions on school choices? At this stage, I'm open to both industry and academia opportunities, maybe leaning towards academia. Regarding research interests, again I'm open. My slight concern is that biostatistics may be a bit narrow to land a job? I'm really not sure about this. Thank you very much!
  24. Which of the following programs is better, an MA in statistics at Columbia University or a Master of Statistic at KU Leuven?
  25. Hi everyone, I have applied to the following schools. 1) OSU (PhD Biostat) 2) MSU (Ph.D. Stat) 3) Kentucky (Ph.D. Stat) 4) Dallas (Ph.D. Stat) How many of you heard back from these schools about funding? Thank You.
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