All Roads Lead to Data: Tony Phelps’s Serendipitous Journey Into Analytics

The path to what you want isn’t always clear-cut—a fact Tony Phelps knows well. For Tony, the journey to his current role was multi-pronged. With his career and personal interests spanning ecology, music, technology, and sales, it’s as if Tony was pulled inexorably toward one clear field: data science. 

His passions would coalesce at The Data Science and Visualization Boot Camp at UC San Diego Extension. Here’s how he got there. 

Tell us about your eclectic background. 

Tony Phelps: One of the many reasons I ended up pursuing the boot camp was where I started: ecology. Initially, I began studying ecology at UC San Diego. I ultimately changed direction, but I stayed connected to the science community and did freelance work in the field throughout my education and beyond.

I earned a degree in 2012 from UCSD in computing in the arts—a program at the intersection of music, visual art, and computer science—with a minor in literature and writing. After graduation, I got a job in the event technology industry. My role included setting up tech for high-profile events like music festivals, brand activations, commercials, photoshoots, and fashion shows. 

I spent six years in this role and fell into a bit of sales within the company due to my writing and communication background. This deeper focus on sales had me working with data and numbers, and I wanted to be able to understand this area better since it was something I worked with every day. 

How did you find the boot camp?

TP: Well, as I became more interested in data, I had a simultaneous realization that I needed to re-tool myself to further my career over the next 10 to 20 years. 

As a UCSD alum, I had a lot of faith that the boot camp would give me the high-level intro to data science and the technical fundamentals that I needed to grow within the field. 

What was the actual program like?

TP:  The program started us working with columns and rows of data, with Excel and VBA; then it progressed into Python, database concepts, JavaScript, data visualization—and even into the deeper waters of big data and machine learning libraries. Luckily, I did a lot of software troubleshooting in my full-time job, which gave me the savvy to figure things out on my own, whether Googling extensively or rifling through mounds of documentation.

The most immediate challenge for me was working 40 to 50 hours a week, attending class, and completing 20 or more hours of homework. I learned how to operate at a higher level from working from the moment I got up to the moment I went to sleep. 

Tell us about the projects you worked on.

TP: I was really lucky to work with such an amazing group of students with backgrounds in biology and atmospheric chemistry. 

For the first project, we used the Environmental Protection Agency’s API to gather pollution data from counties across California to visualize and assess correlations with local asthma rates. It was a really cool project that provided the unique challenges of cleaning millions of rows of data and interacting with a governmental API.  

Our second project was a web scraping application that would cull through the major job posting sites for keywords that may come up in data-focused roles. The application stored the scraped data in a NoSQL database, where it could be used to visualize the highest density of these types of jobs in an interactive Javascript/Leaflet map. The goal was to help people like ourselves get a handle on careers in the industry. 

My final project went back to my initial experience in ecology, where former friends and colleagues of mine had recently finished a large scale coyote population survey in the Mojave Desert. They had collected over 100,000 images from motion sensor camera traps and they had to identify all of the animals in the photos manually. I viewed this as the perfect opportunity to utilize what I learned in class and help automate their identification and analysis process with Python.

What have you been doing since completing the program? 

TP: The field is tough—and I found that a lot of jobs are looking for extensive computer science backgrounds from their applicants. I spent a lot of time building on top of the concepts I learned in the boot camp. And after 5 months, I got an offer for my current position as a marketing analyst with Kia Motors America.

This role has been the perfect entry point for me. I’m able to automate so much of the existing Excel-based reporting with Python that my team views me as a rockstar! Within the next year I’m hoping to grow into a data analyst or junior data scientist role and continue learning the real-world applications of data solutions. 

Aa for anyone considering taking the plunge into a data boot camp, it can be the most challenging and rewarding experience if you invest your energy into it. There are so many free resources online to learn data science, but none can offer the peer-to-peer experience and career coaching services that the boot camp can.