“There are only two hard things in Computer Science: cache invalidation and naming things.”
— Phil Karlton (via Martin Fowler)
Learn the discipline between content design and data engineering.
Featured Course
Full-Stack Clarity: Content Design Meets Data Engineering
Format: 14-week university course (also available as 8-week intensive) Prerequisites: None technical — structured thinking and curiosity required Tools: VS Code, SQL, Microsoft Fabric (free trial), dbt Cloud (free tier), GitHub
The fastest-growing gap in tech isn’t between code and copy. It’s between what data means and what people think it means. This course teaches the discipline that bridges that gap.
“Even though it might seem simple, in reality the ‘Revenue’ line you’re showing could mean dozens of things.”
— The dbt Viewpoint, dbt Labs (source)
What you’ll learn
- Write SQL queries to explore, validate, and audit data products
- Design naming conventions, glossaries, and data contracts
- Build and document a semantic model using industry tools
- Apply content design frameworks to data interfaces
- Evaluate data quality through a content design lens
- Produce a portfolio-ready artifact that demonstrates both content and data skills
Who this is for
- Content designers & UX writers expanding into data products
- Analytics engineers & data analysts who want better documentation practices
- Career changers from writing, journalism, or communications entering data
- Product managers on data-heavy teams
- Graduate students in data science, HCI, or information science
The arc
| Unit | Weeks | Focus |
|---|---|---|
| Foundations | 1–3 | Why words matter in data — naming, schemas, content design principles |
| SQL as a Content Skill | 4–6 | Query literacy, validation, aggregation as editorial judgment |
| The Semantic Layer | 7–9 | From glossary to semantic model — dbt, Fabric, metrics design |
| Data Products Need Content Design | 10–12 | Data catalogs, writing for AI agents, the data product audit |
| Capstone | 13–14 | Build and present a fully documented, validated data product |
Capstone deliverable
A portfolio artifact that includes: a documented semantic model, a glossary and style guide, validation queries proving the docs match the data, a content strategy for the data product, and a narrative reflection.
Interested in bringing this course to your university or organization?
Get in touch →
Coming soon
SQL for Content Designers
4-week intensive
Query literacy for writers. Not a SQL bootcamp — a course that teaches SQL as a tool for asking questions, validating terminology, and auditing data products. Every query you write comes with a plain-English annotation of what it asks and what the answer means.
Naming Things: Terminology Design for Data Products
Weekend workshop
A hands-on workshop on naming conventions, glossary design, and data contracts. You’ll rename a messy schema, write the rationale, and build a style guide. All in a day.
The Data Product Audit
Self-paced assessment framework
A systematic framework for evaluating any data product’s content layer: naming, documentation, error handling, discoverability. Walk through the rubric, score a real product, and deliver recommendations.
For universities and organizations
I’m actively looking for university partners to offer Full-Stack Clarity as a fall semester course or continuing education program. If you run a program in data science, information science, HCI, digital media, or business analytics — let’s talk.
I also offer custom corporate workshops tailored to your team’s data products and terminology challenges.
The book behind the course
The ideas in this course are becoming a book: Full-Stack Clarity: Why the Most Important Line of Code Is the One That Explains What It Does.
Learn more about the book →