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bayessays last won the day on November 25

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    2013 Fall

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  1. https://projects.iq.harvard.edu/stat110 The handouts and practice/solutions tab have lots of good practice.
  2. Should I delete this? It's an interesting change of pace.
  3. The line between ML and statistics is pretty blurry, but if you want to do things like deep learning, computer vision, NLP, a CS department will probably be a better fit. You'll get plenty of practice doing things like linear models in statistics, if you consider that ML. There are obviously exceptions though.
  4. I'm not sure of the exact admissions data, but Berkeley has a biostat PhD with some very excellent crossover faculty like van der Laan that fit your research interests. If location is that important to you, it might make sense to apply to the biostat program (although I think the statistics program is bigger, and you definitely have a shot at it, so I'm not sure whether this is a good idea. UC Davis has a good program and is sort of nearby, and UCSC also has some really good people, but if you're open to going further from the Bay, you don't need to go to programs ranked this low. If you're staying in the US and want to go into industry though, you'll have good prospects coming from any decent PhD program, at least in the technology industry.
  5. Assuming your grades from your master's are good, you can definitely apply to any school. You'll probably get into top 10 programs but I would add some safer programs in the top 20 too. Don't worry about a B. Nobody will think anything of it with the rest of your record. You clearly have some coding skills, so no, not have programming classes will not matter.
  6. I'm not saying TAMU or Purdue are in bad places. Most would just consider the towns to be pretty boring. I meant that Cornell is an Ivy League school in a liberal state, whereas TAMU is in rural Texas, which is more conservative. Very different environment in terms of religion and politics, so if that's an important factor, you may want to see which environment you'd be more comfortable in.
  7. The big conferences are national, so you'll have to travel to them anyways. You should look into whether the department helps students fund their travel. But you will have to determine for yourself whether the small town is good for you. Texas A&M is very much a small college town in a pretty conservative part of the country, a couple hours a way from a big city. Cornell is in a small city too, but very culturally different. Some people consider Bloomington, Ithaca and Charlottesville as pretty desirable college towns, whereas Purdue and TAMU are in towns where most people would not choose to live.
  8. You'll find censored data/high dimensional people at a ton of places - Minnesota has a big focus on HD machine learning (think Lasso). For software, UC Davis has a major R guy, Duncan something. For spatial, look at Ohio State (and lower down, Mizzou). For functional data, FSU has a huge group of people working on shape analysis and a wavelet guy - one of their grads is also a prof at OSU now.
  9. I think most people would consider Seattle to have better weather than most the US. It's a little cloudy, but the rain is greatly exaggerated and the climate is much more moderate than the Midwest or Northeast. The cost of living is the main issue there.
  10. That's tough, I'm not sure there's a forum consensus on whether domestic students should submit GRE math scores around 70% Most people lean towards only submitting 80+. You're right on the border, so it probably won't help or hurt a ton.
  11. To be clear, I think it's reasonable to apply to those top schools besides Stanford even if they are reaches.
  12. I think the range of schools you're applying to is fine, although I don't think you'll get into the top 6 on your list. Agreed on adding more 20-50, although you could go higher. Your low GPA won't be a huge issue because you obviously turned it around after freshman year. I wouldn't bother explaining.
  13. And all that is true. But these are the exceptions to the rule. You are seeing EE students who do statistics stuff, but you're not seeing the 90% of them who don't do anything related to statistics. You're seeing the applied math student who took a class with you, but not the 90% who have no interest in stats. You can do statistical ML research in a CS department, but the focus of the classes and research is so different that most people in such programs will have nothing in common.
  14. I think the OP is really overstating the similarities here. There is overlap, but 90% of the professors in these departments do things that are not statistics and the core set of classes is very different. If you know you want to be a statistician, you will have the most options in a statistics department. It depends a lot on what you're interested in.
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