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About untzkatz

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  1. I am in pharma and I am leaving soon because of the things you listed, because they weren't good fits. I did manage to recently find a more Bioinformatics DS drug discovery type focused position, and they are willing to train me on the domain knowledge thankfully, also because probably my undergrad background is also in a biomedical field so I wasn't a pure biostat/stat person. The TLDR is if you really really want to do stats, data analysis, and modeling focused work, then Biostats in pharma will be disappointing. You don't have to go to big tech though, you can look for something like
  2. Related to this, if your GRE is expiring this November, does sending it earlier, even if you turn in the app itself later but by the Dec 1 deadline make the scores valid?
  3. Damn, so the job description really is deceiving. I guess not too surprising considering how hyped up DS is. In regards to transitioning out of biotech, one issue is my undergrad was in BE. So compared to perhaps other Biostat people who came from stat/math, I feel a bit more "holed into" this industry. All of these Bio-X fields seem to suffer from this. Like my whole resume is projects that are biomedical stats related, even research experience I got in grad school was doing stats for a lab in BE (hence the work with imaging data). Though this issue is kind of common to any Bio-X field.
  4. Well I know Python so it would probably be improving SQL. Are you basically saying that such job descriptions look like they have lots of cool modeling, but that reality is not the case and it just seems that way on the outside? I keep hearing that for statistical/ML modeling jobs these days you need a PhD, and even still it'll be competitive as you said. Modeling 20% of the time isn't too bad, but I'm afraid data analyst it'll be like <5% of the time and most you will be doing is basic univariate summary stats and visualizations. Sounds like what you are getting
  5. So you think that it's not worth doing a PhD in a DS/related field in order to eventually go for one of those more stat/ML modeling based jobs (despite how scarce they seem to be)? A lot of these, like for example the Harnham one posted earlier, seem to require a PhD. Would you say apply for that stuff even with an MS and try to demonstrate that you can do it on the resume/Github & interview? I understand data wrangling is the 80% of data related work, but still I'd like to get away from the regulatory writing aspects primarily. I'm not complaining about data wrangling as much as the bios
  6. I see, yea I am not interested in overall CS though. I feel like I only like this narrow ML/DL area and to me it seemed like stats. So seeing that NYU DS you can more or less just focus on that area looks appealing. They do a lot of MRI research in the biomedical track too, which seems to be more applied statistics based than CS. I do agree more math background would help but I was in a different biomedical field in my undergrad, so can’t do much now. I could potentially sign up for one of either Real Analysis or Data Structures&Algs for the summer though, which I am considering. Hones
  7. Yea I get this perspective too, which brings me to the elephant in the room—Why has DS/ML/AI been so hyped up? Its certainly starting to sound like the “instagram effect” but for jobs. You see the best, most cutting edge stuff (analogous to seeing highlight reels and heavily curated/edited pics ) from the outside but the reality isn’t like that, and it gives you a skewed view. It sounds like this stuff is really more in research labs and if what you are saying is true, it actually does not pay well (since its in academia) except for the very few who are competitive to get a FAA
  8. Data analyst traditionally is like Tableau and SQL from my understanding. That probably doesn’t have much classical nor ML analysis at all. Don’t think its necessary to do DA to go to DS coming from Biostat is it Im currently actually talking to a biostat position related to imaging data analysis though in academia, I had actually landed it last year but I chose industry due to the pay. It wasn’t directly an imaging position, but I had gone through the process and the labs I would have worked with were radiomics and stat learning ones. I decided recently to contact the main person aga
  9. Looking at the PhD DS curriculum here https://cds.nyu.edu/phd-curriculum-info/ Looks like there is a 1 probability course and 1 more modern inference (like graphical models) course. The probability course seems to have notes here https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf and it looks pretty much like MS level probability (looks like over here the MS students also take this) which I have done before already. Its not measure theoretic probability. The intro to DS course is programming based, and the ML class looks like it goes more into ESLR stuff which I don’t
  10. My stats/biostats program in grad school didn’t have this grade inflation. It was graded more like how undergrad courses would be on curves. Actually many Americans in particular got similar scores as me, the international Chinese students (who were like 90+% of the dept, which I think isn’t uncommon) set the high barrier. There were classes were I did decently well and then last minute got screwed by the Final Exam curve. Some of these international students had done things like Quadratic Forms way back in HS, and lot of the MS math stat courses were just review for them. Ok mayb
  11. B/B+ was in graduate MS level math stat classes, not undergrad. Its the classes taken by MS students and the 1st year PhD students who need to review the MS level before doing PhD level inference courses. We used Casella and Berger. My undergrad was in a different biotech related field. The highest undergrad math course I have taken is upper division linear algebra but I also got a B+ there, never did real analysis. I did struggle in the MS level math stat asymptotic theory type proofs. I got As in the computational courses (comp stats and 2 ML classes) though. How important is the stati
  12. Oh I see, well I did do medical imaging related biostat research in my MS. It was interdisciplinary and I got 1 applied paper in a well known MRI journal, although it was more in applied classical stats. And that is the sort of stuff I want to do, involving DL/ML and imaging data. I don’t want to do vanilla biostats stuff like survival analysis lol, even in survival nowadays people are analyzing full images and using the survival loss functions in DL.
  13. 26 itself isn’t old to be in the middle of PhD already but I see it as kind of old to start, like assuming it is 6 years (and given ill have to apply coming Fall) I would be around 33 after graduation. Lot of people are starting to settle just about now. And yea agreed it is a big consideration. But it sounds like the research scientist jobs in FAANG need one. Though I probably wouldn’t want to work for FB but that is more for my own reasons like not being into social media lol. Wrangling data is tedious at times but its still better than writing regulatory reports to the FDA and documenta
  14. Thanks for the links. The Cytel one looks like it uses SAS and nothing cutting edge is being done in there lol, even a log transform is insanely cumbersome vs R/Python/Julia. But these are interesting otherwise especially the Harnham one is right up my alley, though it says Senior DS and wants a PhD. Rest seems mostly director level. The PhD seems to be a big barrier and I am 26 so am getting older. I regret not doing it earlier, as it seems with an MS you mostly get all the boring work especially in biotech. Biotech seems to value the PhD status a ton. Maybe at the end of the day a job
  15. To me ML=statistics, and after taking 2 ML courses in the stats department during my MS I am convinced lol. The rigorous stats you are referring to like missing data I think doesn’t really come up in MS level biostat. And people are conservative when it does anyways, they often just drop it and don’t do fancy imputation. Power and sample size calcs come up quite a bit but they are very straightforward simulations. Also, statistics is not just hypothesis testing and uncertainty quantification to me. I think this is a misconception. Or rather, the Biostat work does not really inv
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