Hot take: Teaching technical skills in 2026 is backwards. Start with communication instead.

Last week I wrote a super long blog post about how I turned my WNBA TikTok into a summer course at UW. It’s been great to hear everyone’s feedback! Lots of people said they wished they could've taken it, and plenty of folks just thought the whole thing was cool.

Full disclosure: I'm job hunting right now, so I'm being forced to articulate my teaching philosophy in a weirdly formal way. But honestly, it's the same philosophy I use when making TikToks! Communication first, technical complexity second. Define your terms. Use examples. Don't assume people already know what you're talking about.

So here's a slightly more polished version of how I think about teaching data science in 2026, when anyone can ask ChatGPT to write code but many still can't explain what that code actually means.

When I asked students in my sports analytics course to explain a statistical concept to a general audience, most of them… didn’t do so great. Not because they didn't understand the math (they did!) But because they wrote like they were talking to other math majors, using jargon and skipping steps that seemed "obvious" to them.

This was intentional. I wanted to see their baseline before teaching them the most important skill in data science: communication.

After we spent class time breaking down what makes explanations clear (define your terms, use concrete examples, don't assume prior knowledge), students revised their work. The transformation was clear. They added definitions (with clear formatting!) where they had previously assumed knowledge. They included examples where there were none. They restructured entire paragraphs with their target audience in mind.

Here's what I learned teaching this course: In 2026, the bottleneck in data science isn't technical skills. It's communication.

With AI models like ChatGPT and Claude readily available, any student can generate code to analyze data. The hard part is knowing what question to ask, interpreting what the analysis actually means, and explaining why it matters to someone who hasn't read your code.

That's why, in my course, communication came first. Students learned to articulate their thinking clearly, define terms precisely, and use examples that make abstract concepts concrete. Only then did we layer in the technical tools (R, Python, Excel) to execute that thinking.

My experience creating sports analytics content on TikTok taught me that small details matter enormously for comprehension. If I don't define my terms, people get stuck in the comments. If I skip a calculation step, they fixate on what's missing instead of understanding the bigger picture. I've learned to anticipate questions and address them before they're asked.​ It’s a delicate balance because you don’t want to overwhelm your audience with too much information that they scroll away, but you also want to provide enough context to convey the full story without losing them.

Many of my TikTok followers say they "don't understand math at all" but can follow my videos. That tells me something important: people who don't identify as "math people" absolutely can engage with quantitative concepts. They just need to see them presented differently.

Sports is often that entry point. It provides built-in storylines, accessible data, and baseline familiarity. But the skills students develop (data literacy, critical thinking, visual storytelling) work everywhere.

What stood out most in student feedback was that this was the first math class that explicitly taught them how to communicate technical concepts to non-technical audiences. One student called it "a crucial skill for mathematicians" that most courses never cover.

​They were right. And it's exactly what I want to keep building on.

Thanks for reading! If this resonated, I'd love to hear from you. And if you know of teaching or analytics roles (especially in sports!), reach out: [email protected]