(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.)
(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.
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.
