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MynahK

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  1. I was attempting to make the thread as broadly applicable as possible. That's where the unusual title comes from. ...Perhaps the level of generality I was originally aiming for with this thread is unrealistic. I am applying to computationally-oriented programs in cognitive neuroscience and psychology.
  2. The present structure of my SOP, described at a general level, is presented in order below. The numbered parts do not necessarily represent breaks in paragraphs - instead, they simply represent topics covered. I have not seen any other SOPs structured in this way, so I am hoping to hear others' thoughts on this particular structure, and on SOP structure in general. My current structure: 1st: Describe ultimate/overarching research goals (my 'big question', followed by a somewhat narrower sub-question). This brief section of the SOP resembles the sorts of 'research statements' that graduate students and faculty members might present on their webpages, before discussing the highly specific problems they've been working on. 2nd: Briefly describe research philosophy and general research approaches (used to tackle the sub-question presented in part 1) that interest me the most... leading in to the 3rd part, which describes how I have begun 'acting on' what I've described in this part. 3rd: Describe research experience, connecting this research to the 1st and 2nd parts whenever possible. Explain how this experience has helped me to develop/focus my research interests. 4th: Explain why I am interested in the particular department to which I am applying, with reference to the 1st and 2nd parts. Mention research topics I am interested in pursuing, and faculty members at this department who are working on these research topics. _____________________________________________________ I should note that I am in a field with many options and approaches available, so parts 1 and 2 in the above structure should communicate research fit with the department in general and with a number of the faculty within the department, while not appearing too narrow.
  3. Note: In considering the comment below, keep in mind that my goals have changed since the first post in this thread. I am interested in data science, machine learning, and computational statistics, and I plan to pursue a Masters-level degree. In keeping with the general idea that courses matter much more than 'major/minor' titles, I am considering dropping my minor in Mathematics so that I am less constrained in my course options during my last year as an undergraduate (note that probability and statistics are taught separately from mathematics at my school). If I do this, I will have a choice between the following this coming semester: [A] Take more computer science courses (Data Structures and Algorithms) .... My background: I frequently program in Matlab and Python (both higher level languages than what is taught in my school's CS department, so I'm a bit rusty on C and Java), I've taken the first two courses for CS majors at my university, and I've also taken free basic courses in CS, Artificial Intelligence, Machine Learning, and Computational Neuroscience via Udacity and Coursera. Take some applied statistics courses (e.g. Nonparametric Statistics, Time Series Analysis, Regression Analysis) [C] Take proof-based Linear Algebra (the upper-level version of the intro-course at my school). However, I've recently been warned by an older Mathematics student that the proof-based course I'm considering is "not significantly different" from the lower-level Linear Algebra course at my school... and that I might be better off 'reviewing' through online lectures (e.g. Strang). The fact that Statistics and Math majors at my school are offered the option of taking either the lower-level or upper-level course seems to support the idea that there is likely to be too much overlap to justify taking the course. Any advice would be much appreciated!
  4. I am about to enter my senior year at the University of Pittsburgh as a Psychology Major and Math minor in the school of "Arts & Sciences" (the university's main liberal arts school). I plan to apply to both quantitative/'computational statistics'/modeling-oriented Cognitive Science PhD programs, and Statistics MS programs. I have been trying to explore my interests as much as possible within the constraints of my Arts & Sciences major and minor, but it is becoming increasingly difficult to do so. For example: during my final two semesters, I would like to be able to fit in some courses related to machine-learning, statistics, computational neuroscience, and cognitive science (otherwise, I have been self-studying these topics as much as possible). Instead, I will be taking courses in clinical/counseling psychology (I saved most of these sorts of major requirements for my final year). The "College of General Studies" at my school has a "Self-Designed Major" program. I am entertaining the idea of switching to this school. I would hope to receive approval and supervision (from a local faculty member whose research I admire) for a self-designed major in something like "Cognitive Science" so that I could take more advanced courses in my areas of interest (subjects relevant to my current and [prospective] future research). This would make my overall course-collection similar to that of a graduate of a quantitatively-oriented Cognitive Science major at a university that offers such a major. The only thing holding me back is the worry that perhaps graduating with a Cognitive Science BS from a "College of General Studies" rather than the "Arts & Sciences" school might somehow hurt my chances of admission to graduate programs. I'm inclined to think that this (a matter of labeling) shouldn't matter... but my question is whether it is likely to matter in fact.
  5. I am planning to apply to 3-4 PhD programs in a sub-field of a science (which I'll call Z) that makes heavy use of computational statistics. At the same time, I will be applying to 2-3 quantitative masters programs in computational statistics and closely related areas. I suppose this situation is analogous to: 1) a Physics student applying to both Physics PhD programs and Applied Math or Computer Science masters programs 2) an Economics student applying to both Economics PhD programs and Statistics or Finance masters programs I've been working in a research lab in the science Z, and at some point this year I hope to ask the professors I've been working with for letters of recommendation. I'm not sure how common it is to ask for letters to graduate programs in different disciplines... but I wonder if there might be reason to worry that a professor might perceive this as a lack of absolute commitment to a particular discipline, and in turn perceive this in a negative light. Any suggestions/advice?
  6. Thanks for your response, Noco7. I should probably add that upon further reflection, I'm not so sure that a career in academia is my goal. My interests are mainly in applied/computational statistics (especially as applied to 'life sciences') and experimental design. I'm currently leaning toward applying to masters programs in Biostatistics, rather than applying to PhD programs... with the goal of ultimately finding work in 'industry'. Any thoughts on taking ODEs vs upper-division Linear Algebra given the courses I've already taken? This question is not solely a "what might help my application" question, but also a "what might be more helpful in preparing for future studies" question. I also remember reading somewhere that when Statistics/Biostatistics programs list "Linear Algebra" as a prerequisite, these programs are usually referring to a proof-based Linear Algebra course (as opposed to the common offering for engineering/science majors).
  7. Next semester, I have the option to take either Ordinary Differential Equations or upper-division Linear Algebra (I've previously taken "engineering, non-proof-based Matrices and Linear Algebra" and "Numerical Algorithms for Linear Algebra"). Given that this will be my second-to-last semester of undergrad, and I don't have much flexibility in my schedule otherwise, do you think future-me would thank past-me for having taken a theory-oriented course in Linear Algebra instead of ODEs?
  8. Previous math courses: - Calculus I-III - Linear Algebra - Numerical Analysis: Linear Algebra - A 'proof course' (prerequisite for Real Analysis) I've also taken basic mathematics courses required of Computer Science students. JZappa, thanks for this advice - I likely won't get the chance to take a (formal) course in Measure Theory before graduation, but I do plan to take Real Analysis (...though perhaps due to scheduling difficulties, I might only be able to take 1 semester before graduating). Thus, I will not quite match the 'ideal sub-ideal' situation you've described. One possibility is to apply to a PhD program at a later date, and fill in some gaps in my background in the meantime. By default, I would approach this with a self-study strategy. Another possibility, of course, is to possibly shift my attention to other (similar) research areas to which I might be better suited given my background.
  9. I am currently in my Junior year (2 remaining semesters), majoring in Cognitive Psychology and minoring in Mathematics. As I've become increasingly engaged in cognitive research and computational/probabilistic modeling over the past year, I've developed a strong interest in statistics and probability. My interests range from the study of stochastic processes and statistical algorithms to issues in research methodology and experimental design. My interest in statistics has also developed by reading online writings by statisticians such as Andrew Gelman and Cosma Shalizi, and reading methodological papers in the cognitive sciences. In a few months, I plan to begin working on graduate applications. I am interested in pursuing a PhD in Statistics... and yet given my background, I am not certain how realistic a goal this is. I hope that others on this board might be able to provide some perspective, and additionally, perhaps some advice on what course of action I might take in pursuing this goal. Since I am interested in a PhD, I am under the impression that first applying for an M.S. program might not be the best plan (especially considering the fees involved). On the other hand, I also imagine that I am at a disadvantage of sorts when 'competing' against other applicants with undergraduate majors in Statistics or Mathematics (both in terms of admissions and in terms of knowledge/preparedness). It is a bit late to change my undergraduate major, but I will take as many relevant courses as possible during my Senior year: (e.g. Probability Theory, Real Analysis, Mathematical Statistics, Stochastic Processes (if my schedule permits) ). Any advice would be much appreciated. If you think that I would be better off not trying to apply to a PhD program straight out of undergrad, what might you suggest instead?
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