Colin VanTassell

For two decades, I've done creative work in theatre, film, and television, on projects ranging from small plays to big-budget Hollywood productions. I love the variability of this work, the necessity to pivot and adapt, to solve new problems and acquire skills on each project. Recently, I've made an even larger pivot. I've embraced the opportunity to pursue a new career in data science. But that doesn't mean I'm starting over. I'm building on the tools that I've been honing my entire career.

I'm coming with the same creative problem solving that brings stories to life, into an industry with growing opportunities and meaningful impact.

The Practicality of an "Impractical" Degree

At first glance, a theatre degree looks impractical. You get to play dress-up, dance around, and laugh your way through college, right?

Well, yes, you do. But you are also getting a holistic education in how to connect with others, understand the world, build something new, and make change happen.

It starts with text analysis, but not the kind you did in high-school English. You build prop lists from stage directions. You track character motivations to understand what drives people to success, to madness, to sacrifice. You study the human condition not to write a paper about it, but to embody it. To make an audience believe it. That is applied psychology. That is learning what people actually need.

Then there is the technical side. You start with a blank stage and a script and build a world from the ground up. Carpentry, welding, painting, upholstery, prop fabrication, lighting design. Every new production means researching a different time and place and understanding it completely. What did people eat in 1920s Chicago? How did they sit? What was in their pockets? You become an expert in things you had never heard of six weeks earlier, because opening night does not care about your learning curve.

And you do all of this inside a creative hierarchy. Everyone is there to serve the director's vision, but you are expected to bring solutions, not problems. To understand how your work affects other departments. To speak different languages and translate between them. You learn how to be both a specialist and a collaborator, and how to deliver under pressure when failure is public and immediate.

The Transition to Data Science

This matters in data science because the technical skills are honestly the easy part. You can learn Python syntax. What's harder is understanding the actual problem you're solving, the people you're solving it for, and how to build something that works in the real world. It's knowing how to research unfamiliar topics quickly. How to talk to both technical and non-technical people. How to deliver when there's no time for a do-over.

That's why I'm currently pursuing a Post-Baccalaureate in Data Science at Cal Poly Humboldt. I already know how to solve problems, tell stories, and work with people under constraints. What I'm adding now is the technical toolkit. Python, statistical analysis, machine learning, data visualization.

I'm not learning how to think about problems. I'm learning the specific tools that let me solve them at scale with data. Two decades of problem solving and storytelling, plus the technical ability to work with modern data systems. The thinking was already there. Now I'm building the technical layer on top of it.

What I'm Looking For

I'm seeking internships and opportunities that sit at the intersection of analysis and creativity. Projects that need both careful thinking and inventive problem-solving. If you're working on something like that, please reach out.