When I was almost finished with my undergraduate degree at WPI, I got to do a senior project about ranking athletes and sports teams. It was my first exposure to logistic regression (I had very little understanding of what was actually going on). But I absolutely fell in love with the project. Which led me to fall in love with statistics.
I was on pace to finish my bachelor’s degree in 3 years (not a big deal), but I wanted to do something to postpone the real world for at least another year (as any rational 20 year old would do.) So I applied to graduate school in applied statistics at WPI. I was equal parts really interested in statistics, really interested in not getting a job yet, and really unprepared for graduate school.
I struggled through 2 years of mathematical statistics, bayesian analysis, linear regression, etc. And I graduated with a less than impressive GPA, but the important part is that I graduated. Towards the end of my time at WPI remember having a conversation with my advisor and I told him that I wanted to go on and do a Ph.D. I assume he thought I was nuts because I hadn’t exactly dominated my way through the program. But he never told me I shouldn’t go. He did tell me that I didn’t need a Ph.D. to work in industry. (Which is solid advice.)
I moved on and worked for 2 years in a direct marketing department of a major catalog company building predictive models. When I first started working their I was really excited to build these predictive models. I thought it was so cool (and I still think it’s cool) that you can take data from the past to help you better predict the future. So I asked where the data was. My boss told me it was here. And there. And over here. And also over there, but you had to modify that before you used it. And a lot of it was missing. I thought to myself “Where is the rectangular file with no missing data? I want to build models.” Ahh young Gregory you were so cute. I spent much of my time cleaning and organizing the data, and relatively little actually building the models. But you absolutely need to understand the modeling pieces to do the cleaning and organizing well. Other wise you don’t really know or understand what data you (might) need.
After about a year I had had enough and wanted to go back to school for a Ph.D. in statistics. I wanted to teach statistics and have more control over the type of work that I was doing. I applied to several programs and told myself that I wasn’t going to go unless I got funding. I got into 2 schools right away, but neither was willing to commit to funding. I was pretty disappointed. But at the last moment UConn came through with full funding for me. I was in. Go Huskies?
So after two years in the “real world” I went back to cocoon of academia. I also went back to being broke. Not college broke. But like regular adult broke. (I probably took a 50% pay cut going back to grad school).
I was 25 when I returned to grad school. And let me tell you, 25 is a lot different than 21. For instance, I never skipped a class in grad school at UConn to go to a fraternity event. School is a totally different experience after you’ve worked a full time, 40 hour a week job. You should treat grad school like this (except it’s probably 60 hours a week). After 3 semesters, I passed my qualifying exam, and I finished all of my exams and classes in 3 years. In total Uconn took four years to finish since I was doing research from day 1 (Expect more like 5 or 6 years (or 7 or 8) if you come in without a master’s degree). I graduated and did a post-doc at UMass in genetics, and just recently hit the academic job market lottery and landed a position at Loyola University Chicago. I can’t wait to start.
While this post has been mostly a bio of my experience, my piece on StatsByLopez will contain more of my thoughts on what grad school was like for me and what advice I would give to someone else in grad school for statistics.