Turn your data into decisions that matter

If you're reading this, I'm guessing you already spend a meaningful part of your day working with data. Maybe it's student assessment results, lab measurements, sales figures, or survey responses. And I'm guessing you've had the experience of looking at a spreadsheet full of numbers and knowing — just knowing — that there's something important in there, if you only had the right way to find it.

That's exactly the gap this course is designed to close. Not by turning you into a computer scientist, and not by watering the material down into something that sounds like data science but doesn't actually equip you to do it. The goal is rigorous, practical fluency — the kind that lets you clean a messy dataset on a Monday morning, fit a meaningful model on Tuesday afternoon, and present a clear, honest finding to a room full of non-technical colleagues by Thursday.

Here's what I've learned teaching this material to people across education, research, and business: the concepts that feel intimidating from the outside — regression, classification, machine learning, experimental design — are almost never the real obstacle. The real obstacle is not having a coherent, sequenced workflow that connects them. This curriculum is that workflow. We start with the foundational question every data scientist has to answer first — what are we actually trying to learn? — and we build from there, one well-grounded step at a time.

I want to be direct about what this course is, and what it isn't. It won't make you a professional software engineer. It won't replace a graduate program in statistics. What it will do is give you the tools, the vocabulary, and the judgment to do serious data work in your own domain — to ask sharper questions, build credible models, design experiments that hold up to scrutiny, and communicate your findings with the confidence they deserve. That's a meaningful professional upgrade, and it's available to you regardless of your background.

I built this course because I believe that the people closest to real problems — the teachers, the researchers, the analysts, the managers — should be the ones equipped to investigate them rigorously. You already have the domain knowledge. Let's get you the tools to match. I'd love to have you in the course.