About the Curriculum

Welcome to the Muggs of Data Science & AI Initiatives Lab. We are redefining computer science education by moving beyond cloud subscriptions to a "Local-First" infrastructure. Here, students don't just consume AI; they build with it. Our curriculum, Social Data Science, bridges the gap between mathematics, sociology, and artificial intelligence, empowering students to ask better questions and discover meaning in messy real-world data.

This isn't just about coding—it's about agency. We believe brilliance is distributed equally, even if resources aren't. Our offline, professional-grade compute environment ensures every student commands the power of a Silicon Valley engineer, regardless of their home internet access. By combining rigorous statistical analysis with sociological inquiry, we turn mathematics into a language for understanding the human experience.

We explicitly reject "blind vibecoding" in favor of Spec-Driven Development. Students act as architects, using AI as a thinking partner to design systems and verify results. Whether preparing for early college credit through CLEP Sociology or building their own predictive models, our students learn that meaningful AI learning begins when they stop asking what AI can do for them and start exploring what they can do with AI.

Social Data Science sits at the intersection of math, sociology, and AI. We don't study abstract formulas; we analyze real patterns in people and systems. This course isn't just for future programmers—it shows how AI-assisted coding and qualitative analysis apply to any discipline. Students learn to ask questions, gather data, model systems, and communicate findings.

Math & College Credit
The course fulfills a high school mathematics credit (Statistics & Probability) and builds WorkKeys Graphic Literacy skills. Grounded in sociological thinking, it also prepares students for the CLEP Introductory Sociology exam, offering a path to early college credit.

In this lab, mathematics is living—applied, interpretive, and deeply human.

Viable, Scalable, and Proven.

Schools often ask about cost. A fully offline, professional-grade AI lab for 20 students can be built for approximately $15,000. This is not a recurring subscription; it is a one-time expenditure that future-proofs the classroom for 5+ years. By shifting funds from temporary cloud licenses to permanent hardware assets, districts gain long-term value and data sovereignty.

It Exists Today
This isn't theoretical. Mr. Mugg has already built and deployed this lab. It is fully functional right now, with students running offline, local AI inferences daily. The infrastructure is live, tested, and delivering professional-grade compute power without touching the internet.

The Full Spec (Local-First)
For the full experience, we use a tiered architecture:

• Student Stations: Mini PCs (Ryzen 7 / 32GB RAM) for zero-latency local LLM inference.
• Teacher Core: Custom towers (RTX 4090 & 5070 Ti) for massive parallel processing and batch analysis.
• The Sandbox: An autonomous air-gapped network ensuring a focused, secure learning environment that protects student data.

Chromebook Compatibility
Equity is paramount. While our lab spec pushes boundaries, Mr. Mugg can adapt the curriculum to function on standard student Chromebooks. This ensures that the core principles of Social Data Science—inquiry, analysis, and spec-driven development—remain accessible to every classroom, regardless of hardware constraints.

Blind Vibecoding vs. Spec-Driven Development

We explicitly avoid "blind vibecoding"—treating AI as an authority and accepting code without understanding. That leads to fragile tools and shallow knowledge. Instead, we teach Spec-Driven Development.

In this model, the student is the architect:

• The human defines the specification (purpose, constraints, data flow).
• The AI writes code to meet that spec.
• The human validates, debugs, and revises.

This approach emphasizes reasoning about system behavior over memorizing syntax. Responsibility for correctness stays with the human, not the model.

For a deep dive into the policy implications, read the Brief Policy Report: CLEP Sociology and Louisiana School Performance Score Alignment.

Authored by Seán Muggivan (Nov 2025), this report uses Agentic Policy Combing to align the course with Louisiana’s accountability frameworks. Key finding: A qualifying CLEP score (≥ 50) contributes 150 points to a school’s School Performance Score (SPS), turning policy structures into engines for genuine educational equity.

Seán Muggivan (Mr. Mugg) is the creator of the Muggs of Data Science & AI Initiatives Lab. A New Orleans native with a background in Creative Writing and Social Work, he bridges the gap between humanities and STEM. His experience ranges from youth social work to teaching math across general and special education settings.

Philosophy & Practice
Mr. Mugg champions "scientist AI"—using AI as a thinking partner to empower student ownership. He views data science as a new literacy connecting math, story, and social understanding. Teaching is his destination, not a stepping stone. He believes true innovation happens inside the classroom, where every lesson is a shared experiment between teacher and student.