Tag: pro tips

26
Sep

Pro-tips for graduate students (Part 3)

This is part of the ongoing series of pro tips for graduate students, check out parts one and two for the original installments. 

  1. Learn how to write papers in a very clear and simple style. Whenever you can write in plain English, skip jargon as much as possible, and make the approach you are using understandable and clear. This can (sometimes) make it harder to get your papers into journals. But simple, clear language leads to much higher use/citation of your work. Examples of great writers are: John Storey, Rob Tibshirani, Robert May, Martin Nowak, etc.
  2. It is a great idea to start reviewing papers as a graduate student. Don’t do too many, you should focus on your research, but doing a few will give you a lot of insight into how the peer-review system works. Ask your advisor/research mentor they will generally have a review or two they could use help with. When doing reviews, keep in mind a person spent a large chunk of time working on the paper you are reviewing. Also, don’t forget to use Google.
  3. Try to write your first paper as soon as you possibly can and try to do as much of it on your own as you can. You don’t have to wait for faculty to give you ideas, read papers and think of what you think would have been better (you might check with a faculty member first so you don’t repeat what’s done, etc.). You will learn more writing your first paper than in almost any/all classes.
20
Jun

Pro Tips for Grad Students in Statistics/Biostatistics (Part 2)

This is the second in my series on pro tips for graduate students in statistics/biostatistics. For more tips, see part 1

  1. Meet with seminar speakers. When you go on the job market face recognition is priceless. I met Scott Zeger at UW when I was a student. When I came for an interview I already knew him (and Ingo, and Rafa, and ….). An even better idea…ask a question during the seminar.
  2. Be a finisher. The key to getting a Ph.D. (other than passing your quals) is the ability to sit down and just power through and get it done. This means sometimes you will have to work late or on a weekend. The people who are the most successful in grad school are the people that just nd a way to get it done. If it was easy…anyone would do it.
  3. Work on problems you genuinely enjoy thinking about/are
    passionate about. A lot of statistics (and science) is long periods of concentrated effort with no guarantee of success at the end. To be a really good statistician requires a lot of patience and effort. It is a lot easier to work hard on something you like or feel strongly about.
More to come soon.
18
Jun

Pro Tips for Grad Students in Statistics/Biostatistics (Part 1)

I just finished teaching a Ph.D. level applied statistical methods course here at Hopkins. As part of the course, I gave one “pro-tip” a day; something I wish I had learned in graduate school that has helped me in becoming a practicing applied statistician. Here are the first three, more to come soon. 
  1. A major component of being a researcher is knowing what’s going on in the research community. Set up an RSS feed with journal articles. Google Reader is a good one, but there are others. Here are some good applied stat journals: Biostatistics, Biometrics, Annals of Applied Statistics…
  2. Reproducible research is a hot topic, in part because a couple of high-profile papers that were disastrously non-reproducible (see “Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology”). When you write code for statistical analysis try to make sure that: (a) It is neat and well-commented - liberal and specific comments are your friend. (b)That it can be run by someone other than you, to produce the same results that you report.
  3. In data analysis - particularly for complex high-dimensional
    data - it is frequently better to choose simple models for clearly defined parameters. With a lot of data, there is a strong temptation to go overboard with statistically complicated models; the danger of overfitting/ over-interpreting is extreme. The most reproducible results are often produced by sensible and statistically “simple” analyses (Note: being sensible and simple does not always lead to higher prole results).