Data Science and College


I’m consider myself lucky that I’ve been able to call many places home over the years — Utah, California, Maryland, and DC. I recently had a chance to go back home to the University of California, San Diego (UCSD) where I received my undergraduate degree and spend time with the students and faculty. And I’ll be honest, it was awesome.

Not just because UCSD was also nice enough to give me an award (because I’m the one should be thanking them), but there’s something about being able to visit a campus where so much of your early thinking is framed (especially with your kids in tow). For example, meeting the students reaffirmed my faith that we’re on an awesome path forward if we keep investing in the future. The next generation of scientists, innovators, and artists are breaking new ground in ways that I could have never anticipated (there are also many more people skateboarding on campus which I fully endorse!). And here are my thoughts that stuck with me:

We need make opportunities available for everyone

There’s nothing like being part of the club called being an alumni, but what about those that didn’t even get a chance to try? What about their shot?

We need to think about our education system with greater flexibility. And I’m a prime example, when I first graduated from high school, I wasn’t ready for college. Lucky for me, I had a great community college that got me ready by teaching me how to write and set me up for a love of math and getting ready to make the most of UCSD. There are too many people out there who need a shot. Their access to opportunity shouldn’t be determined by the zipcode they were born in. The answers to these issues aren’t easy. We need to be aggressive and have the courage to make provide opportunities for everyone. As a country we’re a team; and a good team never leaves a teammate behind.

Back in the old lab with Dr. George Sugihara who got me thinking interdisciplinary

Data science has been going on a long time and the fundamentals are interdisciplinary

My first research projects at UCSD were studying data from measurements of sardine populations (thank you NOAA!) and understanding simulations of long, runout landslides (thank you NSF!). The sardine population data was incredibly messy and took me weeks to understand the politics and nuances of how the data was collected (it’s really expensive to send ships out to sea to collect data). On the other hand, the simulation data was clean, but the math behind the simulations were incredibly complex. In both of cases, I had to find faculty and research that would help me progress in my research. The answer? Nearly all of the ideas and techniques I used came from other areas of science. I just had to adapt them for the messy and larger sets of data I was studying. We often forget how much work has already been done in other fields and by spending time with colleagues from other domains, we’re going to go farther together.

People >> data

Data helps us understand problems and find solutions, but we should never forget that it’s about people first. I was incredibly fortunate at UCSD to have programs of concentrations (similar to a minor) in theater and psychology. I also was required to take ethics as part of my general curriculum. These classes continue to some of the most impactful training that I received.

Theater opened by mind to thinking about ways to communicate and how to connect emotionally with different groups of people. One of those classes was Chicano/Chicana theater that taught me a deep appreciation for how we label others and ourselves matter; as well as, the need to tell stories to remind us of our and others heritage.

Psychology helped me see that problems in the real world are messy and there is an elegance in the design of an experiment. To this day, I remain in awe of the results in physiology, consciousness, and how we interact as a social animals.

Philosophy introduced me to Karl Popper and reinforced the need to dive equally deeply in the foundations of math and philosophy. It’s hard stuff that doesn’t seem obvious in how it may relate to directly building technology, but it has for me time and time again.

Ethics class gave me language to talk about hard issues and frameworks to evaluate choices. It opened my mind to the consequences of every choice and the need to diligent in our assessments.

Over the years, the training these classes gave me have helped me through some of the most challenging situations, connect with different groups of people, and appreciate how important the arts are to society. I couldn’t imagine being able to do any work with data without this kind of training. In fact, it’s dangerous to do data science without ethics training. Just because we can with data, doesn’t mean we should. It’s why EVERY data scientist must have ethics as part of their curriculum. And student should have access to the humanities.


I’m very fortunate and grateful for my family. I’ve got three siblings (who never miss a chance to keep me humble), two of which are also UCSD alumni. My wife and kids have been ridiculously supportive from my first stint in government just after being married to relocating the family for our most recent opportunity to serve. I’ve also got aunts, uncles, and cousins (kind of sounds like a Dr. Seuss book which is apropos since UCSD houses his collected works) who are far smarter than me and give me an opportunity to learn about an incredible number of fields.

But there’s another tribe that I’ve got it and it starts with that unique experience of being thrown in a close-quarters with people who come from all different walks of life with the pressure of having to get through an intense set of new subjects. The only way to succeed in that kind of environment is to become a team (just ask any of those that serve in our incredible armed services). This team is a tribe for life and I’m grateful for it. They’ve stayed with me every part of the way of this journey and also have never missed an opportunity to keep me humble. But it’s not for free and you can’t take them for granted. You have to nurture it by giving back. By giving more than you take, the team goes farther.

Whether you’re a data scientist or not, make sure you’re building your tribe around you. Make sure it’s a diverse tribe—ethnically and socioeconomically. Make sure it takes advantage of all the different kinds of life experiences that are out there. Make it interdisciplinary. Make it a team sport.

Leave a Reply