sisyphus1

All, I am very interested in pursuing a phD in statistics and I had a few questions. To give a little background, I was an undergrad Math/Econ major at a decent school (top 10) and my GPA was > 3.9, but this was mostly because I took the easiest classes in order to graduate (i.e. no ‘honors’ level classes, just the bare minimums like real analysis, number theory etc.). I believe I am mathematically apt, but definitely no where near what my GPA would suggest. Upon graduating in 2009, I got a job in quantitative finance, but while I found the applied aspect of the job fascinating, I felt that I didn’t have the necessary mathematical tools. To enhance my quant skills, I decided to do a part-time Masters in statistics, and I will be graduating from the said program May of next year. While I’ve learned a lot from the program, I felt that there was not enough depth (e.g. we learned to use GLM, but didn’t show why it worked), and this led me to look into phD programs in Stats. Now, my questions (sorry for the lengthy background!):

While I am fascinated by Statistics, I don’t see myself in academia – I see myself working jobs either in the public/private sector (not necessarily in finance though) that utilize high level statistics. Will this be a problem in my statement of purpose if I state that I do not want to be in academia? It seems like a lot of Math phD’s explicitly state that the goal of the program is to prepare their students for a career in research/academics.
For many phDs (especially the sciences), undergrad research is paramount – how true is this for Stat phDs? I have 0 research experience (at least in an academic setting).
Related to above, I feel like my recommendations (from my Master’s program), while good, will not be stellar – I did well in classes (my MS gpa is ~3.9) but didn’t really do anything beyond what was required (part of it was because I was/am working full time). My classes were also quite big and I feel that my recommendations will be impersonal. How important are recommendations? Will my work experience be given any (positive) consideration?
Does the GRE matter for the verbal section? I didn’t have time to study for the GREs so my verbal score is on the low side (580), but my quant is 800.
Which universities have a focus in applied statistics?
Finally, given the above stats, do I have a realistic shot at the really top programs (Berkeley, Stanford, Harvard etc.). If not (which I fear will be the case), what is my realistic reach?


Thank you!

I'm in the process of gearing up for the upcoming application season, and I've been doing some research on the different biostat programs. From looking at various departments and dissertation topics of recent grads, it seems like there are two broad subfields within biostats:

1. 'traditional' biostats (focusing on application of statistics in more traditional fields like clinical studies, epidemiology, public health)
2. 'computational' biology (application of statistics in genetics, neuroscience, biomedical sciences etc.)

Most biostat programs seemed to focus on 1 (yale, columbia, upenn, and most other schools whose programs reside in the school of public health), while there are a handful that have a focus on both (harvard, johns hopkins, university of washington, from some cursory research).

I can honestly say that I have a genuine interest in both - while 1 seems less 'sexy', I can see myself being (for example) a statistician for an NGO/Government/UN, using statistics to guide public health policies. For 2, I've taken a few courses in machine learning where I was exposed to examples of machine learning techniques used in the context of biology, and I found them absolutely fascinating. Another way to look at it would be that from a career perspective, 1 seems more relevant, while from an intellectual curiosity/research standpoint, 2 is more appealing.

Given, the above, I was wondering if people could advise on the following:

- is my broad classification correct? or too myopic?
- given that 1 and 2 are quite different, should I mention that I have an interest in both (in my personal statement) or pick one and go with it?
- if I had to stick to a field, it would probably be 2. however, 2 seems like you would need some exposure to biology, and I have none.
- aside from the schools mentioned above, which other biostatistics departments have a good deal of professors working on computational biology-type problems?

Thanks all and good luck to everyone in the upcoming application process.

I'm in the process of gearing up for the upcoming application season, and I've been doing some research on the different biostat programs. From looking at various departments and dissertation topics of recent grads, it seems like there are two broad subfields within biostats:

1. 'traditional' biostats (focusing on application of statistics in more traditional fields like clinical studies, epidemiology, public health)
2. 'computational' biology (application of statistics in genetics, neuroscience, biomedical sciences etc.)

Most biostat programs seemed to focus on 1 (yale, columbia, upenn, and most other schools whose programs reside in the school of public health), while there are a handful that have a focus on both (harvard, johns hopkins, university of washington, from some cursory research).

I can honestly say that I have a genuine interest in both - while 1 seems less 'sexy', I can see myself being (for example) a statistician for an NGO/Government/UN, using statistics to guide public health policies. For 2, I've taken a few courses in machine learning where I was exposed to examples of machine learning techniques used in the context of biology, and I found them absolutely fascinating. Another way to look at it would be that from a career perspective, 1 seems more relevant, while from an intellectual curiosity/research standpoint, 2 is more appealing.

Given, the above, I was wondering if people could advise on the following:

- is my broad classification correct? or too myopic?
- given that 1 and 2 are quite different, should I mention that I have an interest in both (in my personal statement) or pick one and go with it?
- if I had to stick to a field, it would probably be 2. however, 2 seems like you would need some exposure to biology, and I have none.
- aside from the schools mentioned above, which other biostatistics departments have a good deal of professors working on computational biology-type problems?

Thanks all and good luck to everyone in the upcoming application process.

you could spend a part of your SoP delving into a research interest (in your case, database systems). but at the end you could have a paragraph saying that database systems is your current interest, and that you maintain broad interests in other areas (this is what im doing for my SoP).

I'm in the process of gearing up for the upcoming application season, and I've been doing some research on the different biostat programs. From looking at various departments and dissertation topics of recent grads, it seems like there are two broad subfields within biostats:

1. 'traditional' biostats (focusing on application of statistics in more traditional fields like clinical studies, epidemiology, public health)
2. 'computational' biology (application of statistics in genetics, neuroscience, biomedical sciences etc.)

Most biostat programs seemed to focus on 1 (yale, columbia, upenn, and most other schools whose programs reside in the school of public health), while there are a handful that have a focus on both (harvard, johns hopkins, university of washington, from some cursory research).

I can honestly say that I have a genuine interest in both - while 1 seems less 'sexy', I can see myself being (for example) a statistician for an NGO/Government/UN, using statistics to guide public health policies. For 2, I've taken a few courses in machine learning where I was exposed to examples of machine learning techniques used in the context of biology, and I found them absolutely fascinating. Another way to look at it would be that from a career perspective, 1 seems more relevant, while from an intellectual curiosity/research standpoint, 2 is more appealing.

Given, the above, I was wondering if people could advise on the following:

- is my broad classification correct? or too myopic?
- given that 1 and 2 are quite different, should I mention that I have an interest in both (in my personal statement) or pick one and go with it?
- if I had to stick to a field, it would probably be 2. however, 2 seems like you would need some exposure to biology, and I have none.
- aside from the schools mentioned above, which other biostatistics departments have a good deal of professors working on computational biology-type problems?

Thanks all and good luck to everyone in the upcoming application process.

Agreed with above posters - UMICH will most likely provide better opportunities.
Congratulations on both offers! I am aiming to apply to both programs next year - any chance you can post your profile (without being too specific of course)? GRE range, undergrad gpa, undergrad major, previous research experience etc...