Joining the Lab
(The lab is not currently looking for undergraduate researchers!)
We’re rarely 100% certain our lab will be accepting new students in any given year. It depends, as much often does, on funding; if funding for the lab is looking good we’ll probably be looking for more students. However we’re always happy to have students collaborate with the lab and join our lab meetings, regardless.
The barrier is never lack of research projects, but often lack of time. Voytek prioritizes mentoring, and wants to make sure he has time for everyone in the lab.
If you are interested in applying as a PhD student, note that Voytek accepts students from either the Cognitive Science or Neurosciences PhD programs (you can find application information for each here: Cognitive Science; Neuroscience). In neither case are students accepted to join a lab, but rather they are accepted by an admissions committee to join the program to which they applied. During the first year of training, PhD students are expected to rotate in 2-3 different labs to gain some breadth of experience and get to know their prospective PhD mentor. At the end of the first year, students then speak with their prospective mentor to officially join a lab.
Do not apply to the UC San Diego Neuroscience and/or Cognitive Science programs because they are “prestigious”. While this is not to say that desiring prestige is wrong, it’s not the best reason to pursue a PhD somewhere. The best place to pursue a graduate degree is the place that maximizes the following variables (weighted according to your personal taste):
- Does the city “feel” good to you?
- Will you be happy living there for the next n years, during times of frustration and hardship?
- Are the faculty going to support and challenge you?
- Are the faculty sufficiently knowledgeable about the field in which you are interested?
- Does the university support you financially well enough given the local cost-of-living?
Also factor in significant other and/or family happiness!
- Does the department/university/city have an active and welcoming community (social, academic, and otherwise)?
- What is the teaching load expected of the students?
- What is the course load?
- Are there opportunities for mentoring? Social and civic engagement?
Prioritize academic interests and happiness. You can mostly ignore university “prestige,” as it seems to be only weakly correlated with faculty utility in any given domain. Instead focus on the strength of the faculty, scientists, and students.
In short: if you’re not happy, your work will suffer for it. And frankly, prestige isn’t going to make you laugh, go to lunch with you, or carry you through your PhD.
With regards to specific skills/knowledge before entering a lab, we personally believe that the most bang for your buck would come from learning to code. Our lab is Python- and git-heavy, so we recommend learning or brushing up on those skills. You can learn both at Codecademy’s free online courses (Python; git). You might also want to work through materials from lab PhD student Tom Donoghue’s Introduction to Python class (COGS 18) and Voytek’s Data Science in Practice class (COGS 108). You can also read Joel Grus’s book, Data Science from Scratch.
Also, nothing beats good statistical knowledge. Specifically, understanding that the GLM will carry you far. The rest you can learn during your PhD (that’s what the PhD years are for: learning!).
A lot of people ask Voytek about his industry time working as a data scientist, and what they need to do to break into the field. When I was on the hiring side, I always looked to see some portfolio showing evidence that the applicant had done something. So many people would approach me with a resume and say, “here are the classes I took,” but, honestly, so did tens of thousands of other people so why should we hire you?
Thus my advice is to do something.
brainSCANr was a huge help for my data science career for that reason: it was clear evidence that showed we could conceive of an idea and see it through to completion. Even something silly like this analysis and visualization of the “best rap vocabulary” shows that the author can think of a neat idea, run a fun analysis, complete the project, write it up, and visualize it.
That’s what some companies are looking for. If you need inspiration and/or practice, check out kaggle.
As for my data science toolkit:
- Anaconda to manage python and packages
- Jupyter for quick scripting
- Github for version control
- matplotlib + numpy + scipy + pandas + scikit-learn + statsmodels + nltk + BeautifulSoup + bokeh for statistics and data visualization
R is fine, but clunky (in Voytek’s opinion), and Python plays with web applications and allows for interactive data visualization (via bokeh) much better.
Also, Python is used extensively for non-stats/non-scientific programming, so if my students learn Python they have a marketable skill should they decide to not pursue a career in academia.