Geoff Stirling

Head of Industry, Business, & Industrial Markets at Google
San Francisco, California

Tell us a bit about yourself.

My undergraduate studies were in liberal arts and business. I didn't have any computer science experience coming into the Master of Information and Data Science (MIDS) program. I've been working at Google for over 10 years and lead an amazing team of digital media consultants. It's a great environment full of learning and growth. On the home front, I have an amazing wife and family, including our little 18-month-old boy (born two weeks before I started MIDS!).

What initially attracted you to the data science field?

I was really taken by the multidisciplinary nature of the field. I appreciated the myriad ways that thoughtfully derived insights could make a real difference in fields from medicine, to the arts, to philanthropy and business.

Why did you decide to pursue a Master of Information and Data Science?

In short, because I saw it as an amazing opportunity to gain skills that would allow me to grow as a professional and deliver significant value to my workplace. The program is equipping me with a broad range of skills, practices, and tools that will empower me to help shape the future of our business.

Why did you choose datascience@berkeley?

I chose UC Berkeley after having a great experience at all my initial touch points with the program. The staff, faculty, and students are all world-class, and the opportunity to do this as a distance-learning program made it a perfect fit for my family and me.

What is the I School's advantage?

The School of Information’s (I School) advantage is its multidisciplinary approach, bringing together students and faculty from fields across the spectrum. The result is a rich, engaging learning environment that is exactly what's needed to prepare students for an ever-evolving field.

What has been your favorite course at the I School and why?

It's tough to choose from so many excellent courses. If I must, I'd go with the Experiments and Causality course. It is taught by a dynamic professor and is a wonderful blend of theory and practice. It really challenged our class to think about how we would apply the concepts after graduation.

What is an information challenge that intrigues you?

There are so many emerging information challenges and opportunities before us. I think one of the most intriguing challenges centers around real-time personalization and optimization through machine learning. The applications are especially interesting in fields like health care and the sciences.

Which skills and tools covered by the program do you find most appealing? Why?

I'm continually impressed by the sheer breadth of tools and skills covered by the MIDS courses. I was particularly drawn to the machine learning skills as well as the macro-level problem-solving approaches we learned throughout the program (especially important in the data science space). I find both to be a critical part of the future for this field — machine learning because it's reshaping how we tackle challenges and opportunities and macro problem-solving skills because as emerging leaders in this field, how we approach challenges in the future will define what we're able to accomplish.

How are you able to apply what you are learning to your current position?

Completing the program while working full time affords me the opportunity to bring skills and ideas back to my workplace in real time. This has been a great addition to the in-class learning experience. I've found new angles to introduce to ongoing projects that incorporate newly minted skills in machine learning, experiment frameworks, and data visualization.

What surprised you most about the online learning environment?

I was surprised by how engaging and active the online learning environment is. Small class sizes and a dynamic digital environment allow us to break up for group work, facilitate discussion, and present projects seamlessly. You really get to know your professor and classmates well, which was such a pleasant surprise!

What advice do you have for aspiring information professionals/data scientists?

I've learned over the course of the program that one of the most important parts of data science is the ability to think critically about a problem in its entirety and ask thoughtful questions. If we can do that before reaching for a tool or platform, we'll be in great shape at every stage of our learning journey.

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