Dr. Ryan Rosario is currently a Data Scientist at Google, in the Search area, and Lecturer for the UCLA Department of Computer Science. Prior to Google, Ryan has worked at both Facebook and Amazon. Ryan has split his time between industry and academia.
His research, academic and work interests include Machine Learning, Text Mining and Natural Language Processing.
A note from Grant
Math and computer science are like peanut butter and chocolate, they are both good individually, but when you bring them together, you can create something truly amazing. Today’s guest, Dr. Ryan Rosario, has done just that by first getting all the degrees: a bachelor’s in the mathematics of computation & statistics, then two master’s, one in stats and one in computer science and finally a doctorate in statistics, focused on text classification.
After all those years in school, he has steadily built up a career for the likes of Facebook, Amazon, the University of California and Google with titles like Data Scientist, Chief Scientist, Applied Scientist and my personal favorite: Quantitative Engineer.
To this day, he also continues his love of school by serving as a Lecturer in the Computer Science department at UCLA.
Stay tuned as we catch up with Ryan Rosario and delve into his career bringing together my two favorite subjects: math and computer science!
“It wasn’t until I was in the middle of my undergraduate career when I realized how much computer science and statistics went together.”
“For me, security is most important. Although I’ve worked at startups and really enjoyed them, I personally like the bigger company culture better.”
“99 percent of the time as a data scientist is spent with data management. I actually enjoy this! It should actually be a passion for people to enjoy data management.”
“You have to find your own projects on your own using data that’s in your interest area. If you’re interested in the problem being solved you’ll be able to develop the intuition to solve the problem.”
- Many of our listeners are often wonder about pursuing graduate studies after college. Ryan did two master’s and a PhD. What inspired him to go so deep?
- Ryan has managed to keep one foot in both academia and industry. How has he been able to organize this kind of balance?
- Ryan got dual master’s degrees in statistics and computer science.
- What is a quantitative engineer?
- Ryan explains how to develop data intuition.
- Text and natural language processing
- What are some key skills that will help you be successful in data science?
Guide to learning R for data science – https://www.dataquest.io/blog/learn-r-for-data-science/
Check out Ryan’s blog – http://www.bytemining.com/