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Profile Evaluation: MS in Statistics


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I currently work in equity research, and am interested in applying for a master's degree in statistics, with the hope of pursuing a role in data science. I would like to be able to work as a research assistant with a professor during my time in graduate school. I recognize that my application has a couple of red flags that make my application less competitive, and any advice as to what types of programs I may have a chance at admission would be helpful (Does it make sense for me to apply to schools like Rutgers, University of Florida and University of Iowa).

- School: Ivy League, Major: Math

- GRE pending, GPA: 3.5 (I transferred schools before my junior year. 3.5 is the GPA for my junior and senior years. GPA is ~3.75 if looking at all 4 years. Not sure how graduate programs look at GPA, and assuming that freshman and sophomore year grades are not particularly salient anyways.)

- Student type: Domestic, white male from NJ

- 2 bad grades in quantitative courses - C in Calculus IV (Partial Differential Equations) and C+ in a Computer Science course

- Statistics coursework limited to an introductory, non-calculus based statistics course. Will be taking calculus-based probability and statistics at night in the Spring

- No research experience

- Work experience: 1.5 years in asset management. (The work is analytical, but does not involve knowledge/use of statistics beyond the creation of linear regressions

Thank you.

 

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It appears to me that you are not very competitive in terms of academic credentials so you may have trouble applying to most PhD programs or certain "more academic" programs such as Chicago. But your work experience is highly impressive, which is a huge bonus for many data science/statistics master degrees. An example is Harvard. I did my masters at Harvard University so from looking at admitted students, I know they place high regard on industry experiences, as well as prestige of your undergraduate institution. You can apply to Data Science master program at Harvard. Talking to the admission personnels in advance may help (although I didn't). What is your GPA and GRE? These actually matter a lot to admission to a master program contrary to common narratives, probably even more so than research experience unless you have glowing letters or publication. Most people I know here have near-perfect GPA and GRE (exceptions apply). That said, it's not the same as taking crazy graduate level analysis class and get 100. You just need good grades on most, probably not very rigorous, classes you did. 

Edited by DanielWarlock
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I disagree with the above poster. You have a 3.5 GPA in a math major from an Ivy and you’re a domestic applicant. Assuming you get a solid a GRE score, I’m sure you could get into some solid masters programs. I’m not super familiar with masters admissions, but I’d be surprised if you didn’t get into Rutgers. I think you could even get in somewhere ranked higher if you applied to a bunch of programs.
 

By “work as a research assistant,” do you mean funded? Because you will have a hard time finding that. Additionally many statistics masters are focused primarily on coursework and feature basically zero research, so if research is important to you, be sure to look for that.

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I found Daniel's response misleading. A 3.5 GPA from an Ivy is not stellar but good enough for master's admission. Master's programs usually have much higher admission rate than PhD programs and as long as you meet the minimum requirement you will be considered. Coming from an Ivy with decent a GPA helps a lot and I can see you get into top 10 master's programs for statistics.

 

Edited by statfan
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On 10/19/2019 at 9:16 PM, statfan said:

I found Daniel's response misleading. A 3.5 GPA from an Ivy is not stellar but good enough for master's admission. Master's programs usually have much higher admission rate than PhD programs and as long as you meet the minimum requirement you will be considered. Coming from an Ivy with decent a GPA helps a lot and I can see you get into top 10 master's programs for statistics.

 

I'm not sure about this. I was rejected from Stanford and Chicago so did all of my classmates here at Harvard. Acceptance rate at Harvard DS master program is already 7%. And Chicago and Stanford are absolutely lower.  All I'm saying is that for data science/stats programs like Harvard and Berkeley they highly value your professional experience where Chicago and Stanford place high emphasis on your academic credentials. Classes at those two schools are way harder than Harvard and is not flexible at all. You need to do Probability and Inference etc. with PhDs in order to even graduate. I honestly am not sure if I was up to that task 1 year ago and no one will have confidence looking at my engineering transcript. I'm not sure about that even now as they say PhD classes at Harvard, which I took and struggled to a degree, are like joke compared to those schools. But essentially at Harvard or Berkeley you can take easier classes and go find a job so they are less obsessed with you being a math genius.

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13 minutes ago, omicrontrabb said:

@DanielWarlock The original poster is a domestic applicant. You're an international applicant. It is much harder to be admitted as an international student. (He's also an Ivy League math major.)

This is absolutely false at my program at least. Half of the class are international students whereas the competition among domestics is no less stiff. Some guys are domestics from Berkeley math, NYU math etc. Harvard math, MIT, Stanford etc. with 3.9+ GPA. Many outstanding applicants simply don't want PhD and apply to Masters at top schools so that they can transition to elite companies and live a comfortable life in 1-2 years. I don't think my profile is superior to theirs in any way. Top master programs are less selective compared to PhD mainly on their requirement for academic rigour and there is more flexibility, especially emphasis on your prospect to land an elite job. There is not much discount on GRE or GPA as far as I can see. 

Edited by DanielWarlock
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