(This is my third post on statistics graduate school. You can read the first, on coursework, the difference between a Masters and a PhD, and proving your worth here, and the second, on advancing to candidacy, writing the dissertation, and having fun in grad school, here.)
Last week, I got the opportunity to share the highlights from the eyeo festival at the DataVis LA meetup. The eyeo festival is a yearly event held in Minneapolis that draws together artists, technologists, designers, and many more people who work with art and computers. As I said in the meetup description, a lot of the things that caught my eye this year could be described better by the term “art” than straight “data visualization,” although there’s usually both at the festival. I also really liked that this year’s festival emphasized the history of computer art and data visualization. By bringing in Frieder Nake, Lillian Schwartz, and Roman Verostko, the organizers grounded us in the history that started in the 1960s.
(Note: This is my second installment on grad school, you can read the first part on coursework, the difference between a Master’s and a Ph.D., and proving your worth, here.)
Advancement to candidacy # After finishing coursework, the next step in our PhD program is to “advance to candidacy.” This is the formal process of proposing your dissertation research. Our department has a diverse set of research interests, both theoretical and applied, so dissertations can take a variety of forms. This is yet another difference between Master’s and PhD projects– typically, Master’s projects are more directed by faculty members and PhD projects are chosen primarily by the students themselves. Either way, the process begins with a conversation between the student and his/her advisor.
Another exciting development in data science coming from our department at UCLA is a high school class called Introduction to Data Science (IDS). This project has been made possible by a National Science Foundation grant to support Mobilize, for which Rob Gould (mentioned in my previous post on DataFest) is the Principal Investigator.
As a PhD graduate student in statistics here at UCLA, I’d like to talk a bit about our graduate program to give you an idea of what to expect if you’re interested in UCLA or statistics graduate school in general. First, it’s important to note that UCLA does not have a specific “data science” degree program at this point; the department offers a PhD and a Master’s degree, both simply labeled “statistics.” Some statistics departments have made the decision to change their name to “data science,” but that is often just a marketing decision. The UCLA department is pretty forward-thinking and has a focus on computation, so it’s fairly “data science”-y without having that exact name.
Below is the first of several exciting data science developments for the younger generation, happening right here in Los Angeles. This project is unique because it engages undergraduates in real data analysis, something that happens quite rarely in a classroom. If projects like this catch on as a new trend, we’re going to have some real competition for our jobs in a few years!
