Domingos Lopes is about to begin a new job involving machine learning at Google after having completed the NYC Data Science Academy 12-Week Data Science Bootcamp. He came in with a strong math background, having earned a PhD in that field from NYU in 2016. After deciding that he did not want his job prospects to be limited to academic settings, Lopes decided that the best course of action was to acquire data science skills. In addition to the technical skills like Python fluency, he expects to draw on the ability to communicate data insight and work effectively in a team setting as he embarks on his career at Google.
During high school I concentrated on Computer Science, but since then my major has always been mathematics. More than the beauty of it, what drew me towards it was the type of challenges that math research presented. After completing my undergraduate and master studies in Portugal, I came to NYU for my PhD, specializing in Partial Differential Equations.
Having decided that I was going to pursue a career outside academia, at least in the few years following my PhD, and coming from a pure math specialization, there was going to be a gap that I had to bridge. I had a very limited experience with data science, having done some online courses. However, that would get me an internship at best, and I'd end up not with many choices. For that reason, I chose to join a data science bootcamp.
I was certainly familiar with other programs offered in the city, having checked them out in the past, although I didn't do any extensive research on other potential options at the time I decided to join.
There are essentially three factors that were decisive:
I was never bored enough to count the students, but I believe 37.
If my memory doesn't fail me, PhDs accounted for about 20% of the cohort. Among the remaining 80%, a majority had master’s degrees while a minority had a bachelor degree
It was certainly a very diverse crowd that, besides Americans, was mostly composed of Chinese, Indian and European people.
Coming from a pure math background, it's hard to make a comparison, as for many years the focus was much more on the theory and proofs and less so on the applications. Except for a few cases, the classes at the NYC Data Science Academy are mostly focused on the application, while the theory side of things is covered at a more superficial level that intends to be more intuitive and accessible for people with very diverse backgrounds. Classes are always taught with slides, having a lab component towards the end.
I didn't expect that I'd be meeting so many interesting people as, for most of my recent social life, I interacted almost exclusively with mathematicians.
I think every project presented its own challenges, being either how to deal with the amount of data, how to find the right predictive models, or even how to manage work between teammates. I'd highlight as enjoyable having teammates very eager to learn new topics, useful for our projects, that weren't covered in class. As challenging, the nature of my capstone project of predicting the author of paintings.
Working for Google has been a desire I've had for a few years now, given the nature of the problems they work on, as well as for arguably being the best place to work. I'll be working on a team that does a lot of machine learning, which is a very hot and valuable field, and fits well with my mathematical background and passion for coding. As such, I see it as an ideal position, certainly so at an entry level.
Given that I'll be working on machine learning, I believe most of the machine learning topics we learned at the NYC Data Science Academy will be useful in this job. Other useful skills I acquired during the boot camp, that would certainly be helpful, are an improved Python fluency, communicating data insights and the ability to work in teams.
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