Bridging the Gap: Brandon Knox, MSDS ‘26

I have chosen to focus my studies on model interpretability to bridge the gap between complex machine learning models and human understanding. In my time working in finance, I saw how important it is for decision-making algorithms to be transparent and interpretable.

  
Originally from Chandler, Arizona, student Brandon Knox brings notable experience in finance and data analytics to his Master of Science in Data Science. A first-generation student, he finds satisfaction in mentoring, as well as participating in challenging team events like hackathons, most recently taking first place in the Amazon AWS Challenge for Hack Arizona 2025.

What brought you to the University of Arizona to study data science?

I wanted to move beyond my work experience in data analytics, exploring different relationships in data instead of simply summarizing it. The University of Arizona's top-ten national standing in Fortune for its Data Science program also attracted me.

What area within data science most interests you?

I have chosen to focus my studies on model interpretability to bridge the gap between complex machine learning models and human understanding. In my time working in finance, I saw how important it is for decision-making algorithms to be transparent and interpretable. This was extremely necessary for audit purposes, and often necessary to confidently relay data insights to management. One project I did in my free time was build a smartphone app that allows users to pick different values for health metrics (BMI, age, blood pressure, etc.) and show in real time how different values impacted model predictions for a medical prognosis.

What do you like best about the Master of Science in Data Science?

One of the aspects I appreciate about my degree program at U of A is the variety of skills taught in the program. I enjoy diving into the mathematical foundations behind machine learning algorithms and applying these models for data mining techniques. I also enjoy the diverse teaching approaches—some professors emphasize a rigorous theoretical understanding of model mechanics, others encourage group discussion and projects. For a data visualization course, we took turns critiquing each other’s work the way art students do!
 
 

Tell us about being a first-generation student.

My mom was an elementary school teacher, and my dad served in the Navy, developing his skills with radar electronics. Their different backgrounds influenced me to go to college and made me value education and technology in equal measure.

What has been your biggest challenge in the master's program?

One of the most significant challenges during my time in the College of Information Science at U of A has been understanding the complex mathematical concepts that underpin data models. Deciphering formulas and algorithms (i.e., forward/back propagation, eigenvalue decomposition, covariance matrix calculations) and comprehending how they intertwine with other statistical principles is something I continue to explore. U of A’s dedicated professors have helped me demystify these ideas, giving me a deeper understanding of machine learning.

What has been your biggest challenge outside of the classroom?

My biggest challenge outside of U of A came from circumstances brought about by the COVID-19 pandemic. Transitioning to a fully remote work environment posed significant obstacles, and I was promoted to a training role for data analysts during this period. Learning to navigate diverse team dynamics virtually while still contributing to organizational goals was challenging! Through that, I established and facilitated a monthly Python community of practice, which focused on using the language for analysis and data modeling. Topics for each session were driven by the 30+ employees and contractors who attended each month, and I enjoyed putting together code demos and breaking down concepts for applications across analysis, ETL methods and new software being adopted by the company. I was even able to remotely teach a group of high-school students how to use Excel and write code!

Tell us about your job experience at Toyota.

During my time as a programming analyst at Toyota, I really appreciated the idea of data-driven decision-making. I started creating reporting automation in the resource management and workforce management departments, and later wrote code to move data around into dashboards at the enterprise level. I enjoyed building libraries for our teams to use to streamline connecting to various databases and perform web-scraping from different web portals. The dashboards I built were used as levers to make decisions and communicate complex trends. Ultimately, my goal is to bridge the gaps between raw data, insights and useful actions.
 
 

Tell us about your win at the Amazon AWS Challenge for Hack Arizona 2025.

I was honored to be a member of the first-place team for the Amazon AWS Challenge for Hack Arizona 2025. I worked alongside fellow students Hidoyat Ruzmetov and Harper Gibbs to build a navigation app that utilized Tucson’s public safety data to help users pick routes for walks. I wrote text-vectorization and ML clustering code to bucket the many unique crime descriptions we had into well-defined groups, saving us hours of work that would have been spent manually categorizing every unique description found from public safety data. We used these groupings to adjust the app’s route-finding algorithm, weighting more severe incidents over those that might not affect a person’s walk through town. Learn more here and on the hackathon’s official site at hack.arizona.edu. In my professional career I've mentored colleagues, demonstrating practical applications of Python and various ML models in reporting and analysis.

What are your passions outside of school?

Beyond school, my passions lie in art and writing. Creating art has always been therapeutic for me, whether it’s drawing expressive figures or a landscape painting inspired by Tucson’s beauty. I enjoy writing, and like many others I have an unfinished book I’ll get to some day.
 
 

What advice do you have for prospective Master's in Data Science students?

My advice would be to be patient and be self-aware throughout your academic journey. The field of information science is huge and it requires a lot of dedicated time and effort. You have to give yourself enough room to absorb complex ideas. Don't shy away from areas where you may struggle or feel uncertain—note them down so you can revisit them when your mind resets. Slowly, things will become more clear.

 
Explore the Master of Science in Data Science, offered on campus and online, or support College of Information Science students through scholarships and other important programs.