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Found 11 results

  1. Hi can anybody suggest me masters course with dual degree in economics and public policy?
  2. 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!
  3. All, I posted this poll a while back in the psychology sub-forum, and now I realize how appropriate it is here. It helped me look at the things that others are looking at; I'm now more confident in what matters to me. SEE POST HERE
  4. Hi all. Tired of waiting for graduate committees’ decisions I estimated decision timelines myself based on gradcafe data. For each university and program in albums below you will find three graphs: Decision timeline as a cumulative sum of decisions (accept, reject, interview, waitlist) as a function of time between Jan 1 and May 1 for the last five years combined. Boxplots of GRE Q and GRE V for people who reported both scores. Histogram of GPAs (from 2.5 to 4.0 with 0.1 step). Here is the list of programs I analyzed (some important notes below): Computer science PhD https://imgur.com/a/cXaEs Computer Science MS https://imgur.com/a/u3joC Electrical Engineering PhD https://imgur.com/a/ra3Eh Electrical Engineering MS https://imgur.com/a/KUGrD Economics PhD https://imgur.com/a/NzlYm Economics MS https://imgur.com/a/JfgSk Statistics PhD https://imgur.com/a/mB5UC Statistics MS https://imgur.com/a/tXowL Mathematics (applied and pure) PhDhttps://imgur.com/a/d0821 Chemistry PhD https://imgur.com/a/U5x91 Physics (applied and pure) PhD https://imgur.com/a/35tTy Chemical Engineering PhD https://imgur.com/a/Tng2r Literature PhD https://imgur.com/a/LDKpT Anthropology PhD https://imgur.com/a/d5ub4 Bioengineering PhD https://imgur.com/a/RpTSD Philosophy PhD https://imgur.com/a/ihoGS Biology PhD https://imgur.com/a/FWhoD How to use the graphs? I used this data to decrease my own misery. Now that I know decision timelines of universities and programs I applied to, I can refresh gradcafe less and concentrate on more useful stuff more. Also, it is interesting to explore differences between different universities/programs. For example, some universities do gradual accepts rejects/accepts and others do it in waves. Some programs start early (chemistry) and some — later (CS). Keep in mind, that there may be errors in my analysis so use this data at your own risk. How reliable are timelines? I personally trust them (but I am biased). In general, it depends on curve shapes and available data. If there are more than 100 observations overall — I would consider that data to be pretty reliable. If there are characteristic ‘steps’ — it is a good sign because may indicate internal deadlines for waves of accepts/rejects. But the number of admissions/rejections records in the data is definitely inflated by question records (i.e. ‘to poster below: what program?”). I filtered some, but definitely not all of them. Also, bear in mind that department policies can change. How reliable are GRE/GPA? Somewhat reliable. There is noise, mistakes (i.e. switched Q/V) and self-report bias. For example, salty people with good scores may more likely report rejections and lucky people with low GPAs may less likely report accepts. But for some universities which publish admission statistics (for example, Duke), calculated GRE/GPA medians are pretty close to reported averages (I didn’t calculate means, sorry). Also, we can’t affect GPA/GRE right now, so it is mostly for entertainment. How did you do it? Scraped and parsed all gradcafe results. Selected all records from Jan 1 2013 to May 1 2017 and combined data for all years together, so all data is based on five year period. For each university and program in question I built a cumulative sum of decisions as a function of days since beginning of the year. For analysis of GRE I only chose records which included both Q and V scores. For analysis of GPA I used only 4-point scale grades and didn’t convert other scales to it (i.e. 10-point). Selection of universities/programs was done by regular expressions so there can be some noise added by incorrect parsing. For example, “University of Washington” may both mean Seattle and St. Louis. I tried to avoid it the best I could but there can be mistakes nonetheless. How did you choose universities/programs? Voluntarily, so there are a lot of omissions. Sorry, if your university/program is not there. Also, bear in mind that programs may overlap (for example ‘Computer Science’ and ‘Electrical Engineering’). Finally, I excluded uni/program from analysis if there were less than 30 observations. Will you share your code/data? I am thinking about it, but undecided yet. Hope it helps and good luck with the admissions!
  5. So far: Accepted: University of Virginia MS in Data Science Waitlisted: Northwestern MS in Analytics Interviewed: University of San Francisco MS in Analytics Still under consideration, but told "odds are unfavorable": NC State MS in Analytics How about you?
  6. Hi, This is my first post on the Grad Cafe. I'm encouraged by all of you guys because now I know that I'm not alone in this hectic time of grad applications. I am writing SOPs for MS/MA in statistics and MS data science. I eventually do want to pursue a PhD though I'm not sure which aspect of the mathematical sciences I want to be specialize in. My question is essentially: 1) What roles (software, data engineer, analyst etc)would MS data science graduates have in the industry? (whether government or commercial) 2) Would I easily have enough prior knowledge to pass qualifying exams as a Stat PHD candidate coming from a background with an MS data science degree? 3) What are the backgrounds of the data science masters cohorts? (what undergraduate majors, which industries, gpa, research interests etc.) 4) (Optional - for better insight) Finally, what would be your ranking of data science master programs and by what aspect are you ranking them (academic rigor, size of research projects, location etc.) I look forward to hearing your input.
  7. Hi, I worked on my PhD dissertation this summer. My travel was funded by the director of a site. I used a laptop owned by my university to collect this data. Throughout the course of the summer, the computer took a dive and would no longer turn on. This happened through no fault of my own or any other user failure. It wasn't until this happened that I realized I had not backed up all of the data. I am not back in the states and took the computer to IT, they told me that they would need to replace the hard drive, but were unsure of the underlying issue. I took the computer back and started looking into companies that could retrieve the data. I found that if it's a serious problem it could cost upwards of $1000. I, of course, don't have this kind of money. My advisory is now saying that I am responsible for recovering the data no matter what cost, up to the price of the airline ticket bought for me to collect the data. Is this ethical? Yes, I did not back up the data and that is completely my fault, however, the computer was not in great condition before I used it and the malfunction of it is not due to my negligence. Any thoughts would be appreciated. I have more details about the circumstances of the paid-for ticket as well as the type of data if that makes a difference.
  8. Hello everyone, As a form of procrastination I kept track of the anthro program results this year in an excel file. Here are some interesting things I found: - # of anthro submitted decisions = 490 - # of acceptances = 196 (40%) - # of rejections = 294 (60%) - # of programs represented = 109 - Top 10 most applied to programs (# of decisions in parentheses): 1. Michigan (24), 2. Brown (19), 2. Harvard (19), 4. UC Berkeley (18), 5. Toronto (17), 6. NYU (16), 6. Stanford (16), 8. Oxford (14), 9. Princeton (13), 9. UCLA (13) - Top 10 most selective programs (% admitted, at least 5 submitted decisions): 1. Chicago (0%), 2. Brown (5%), 3. Stanford (6%), 4. Cornell (9%), 5. UC Santa Barbara (11%), 6. NYU (12%), 7. Princeton (15%), 8. Emory (17%), 8. SUNY Stony Brook (17%), 8. Notre Dame (17%) - Top 10 most selective programs (% admitted, at least 10 submitted decisions): 1. Chicago (0%), 2. Brown (5%), 3. Stanford (6%), 4. Cornell (9%), 5. NYU (12%), 6. Princeton (15%), 7. Michigan (20%), 8. Harvard (21%), 9. Columbia (25%), 10. UC Berkeley (27%) - February is when the action happens. The vast majority of decisions are made in this month, with many fewer in January and March: -I attached the excel file if anyone else wants to play around with the numbers -As a word of caution, I have no background in statistics and have no idea how the Grad Cafe data relate to the true amount of applications each school received. This is meant to just be a rough idea as to how the application season went and should not be taken seriously. I hope it might help people who apply next year have an idea as to which schools are popular and when schools release their results. grad school national results.xlsx
  9. Hey anthro applicants, It's been approx. one month since programs began releasing decisions and I decided to compile data over that time period to have a look at where we are: Submitted decisions as of 2/6/2017: 98 Accepted: 41 (42%) Denied 57 (58%) Only three major programs appear to be completely finished with admissions, and boy was it rough: Brown (1/16 admitted) Princeton (2/11 admitted) UCSB (1/9 admitted) Many other programs seem to have admitted their top candidates (e.g. Berkeley, Oxford, UCLA), but are waiting to see how the wait list process goes. Hold your fingers. Here's the rest of the data: School Date Accepted? Alabama 12/6/2016 Y Alabama 1/24/2017 Y ASU 1/20/2017 Y ASU 1/27/2017 Y ASU 1/28/2017 Y ASU 1/28/2017 Y ASU 2/3/2017 Y Berkeley 1/30/2017 Y Berkeley 1/31/2017 Y Berkeley 1/31/2017 Y Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/3/2017 N Brown 2/4/2017 N Brown 2/4/2017 N Brown 2/4/2017 N Brown 2/1/2017 Y Cambridge 12/16/2016 N Duke 2/6/2017 N Emory 1/24/2017 N Emory 1/25/2017 N Emory 1/25/2017 N Emory 1/25/2017 N Emory 1/25/2017 N Illinois 1/20/2017 N Illinois 1/20/2017 N Illinois 1/25/2017 Y Indiana 1/30/2017 N Indiana 1/30/2017 N Inidana 2/6/2017 Y Iowa 1/28/2017 Y Maynooth 12/14/2016 Y Michigan State 1/13/2017 Y Minnesota 1/23/2017 Y Missouri 2/5/2017 N North Carolina 1/28/2017 N North Carolina 1/23/2017 Y North Carolina 1/25/2017 Y Notre Dame 1/10/2017 N Notre Dame 1/12/2017 N Notre Dame 1/18/2017 N Oregon 1/23/2017 N Oregon 1/23/2017 N Oregon 1/23/2017 N Oregon 1/24/2017 N Oregon State 2/3/2017 N Oxford 1/31/2017 Y Oxford 1/31/2017 Y Oxford 1/31/2017 Y Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/3/2017 N Princeton 2/2/2017 Y Princeton 2/2/2017 Y Purdue 1/25/2017 Y SMU 1/27/2017 N SUNY Albany 2/5/2017 Y Syracuse 1/31/2017 Y Texas 1/31/2017 Y Texas State 2/6/2017 Y Toronto 1/27/2017 N Toronto 1/30/2017 Y Toronto 2/2/2017 Y U Conn 2/3/2017 Y UC Davis 1/31/2017 Y UC Davis 2/3/2017 Y UC Davis 2/3/2017 Y UCLA 1/27/2017 Y UCLA 1/27/2017 Y UCLA 2/2/2017 Y UCLA 2/4/2017 Y UCSB 1/18/2017 N UCSB 1/18/2017 N UCSB 1/18/2017 N UCSB 1/19/2017 N UCSB 1/19/2017 N UCSB 1/19/2017 N UCSB 1/19/2017 N UCSB 1/28/2017 N UCSB 1/19/2017 Y WashU 1/17/2017 N Wisconsin 2/2/2017 Y Wyoming 12/22/2016 N
  10. I'm an international student from China who recently graduated from UCLA with a 3.5 overall GPA, and a 3.8 statistics major GPA, and am going to start my statistics master's program at Columbia University this Fall. I plan on applying to a PhD's program in statistics after I graduate from Columbia but I don't know if I should retake my GRE I have a 170 (98%) on Math, 163 (92%) on Reading, and a 4 (56%) on writing. I am preparing for my GRE Math subject test but I don't know if I should retake my general GRE because I only got a 4 on writing. My dream schools are: Stanford Berkeley Chicago Harvard Washington Columbia University Since those are all competitive universities to get into for a PhD in statistics, I'm conflicted because I don't know how much getting a 4 on my writing matters? I really appreciate every advice. Thank you all in advance.
  11. Just in time for decision day: the APDA project releases data on how over 100 departments have done placing their students in jobs (including types of jobs and AOSs): http://dailynous.com/2016/04/15/philosophy-placement-data-and-analysis-an-update/
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