Mugg’s Data Science Lab believes that AI education should be about agency, not access.
We don’t study AI to keep pace with machines; we use AI to expand what humans can imagine and build.
Within a secure local network, students learn to become creators — designing models, experiments, and interfaces that reflect their own questions about the world.
The lab shows that meaningful AI learning begins when students stop asking what AI can do for them and start exploring what they can do with AI.
Social Data Science is a year-long mathematics credit course that sits at the intersection of data science, sociology, and artificial intelligence. It was designed for students who want to understand the world not through abstract formulas, but through the real patterns that emerge from people, communities, and systems.
This is not a course that studies AI for its own sake. We learn what we can do with AI—how it helps us ask better questions, discover meaning in messy data, and make sense of our social world. Every concept connects back to human experience, making mathematics a language for understanding rather than memorization.
Social Data Science fulfills a high school mathematics credit by aligning with Louisiana’s Statistics and Probability standards. Students gather, clean, visualize, and interpret real-world data, applying probability and statistical reasoning to social questions that matter. The course also builds WorkKeys Graphic Literacy skills, preparing students for both college and career.
Because the course is deeply grounded in sociological thinking, students also build the knowledge base and analytical skills needed for the CLEP Introductory Sociology exam—giving them the potential to earn college credit before even enrolling in college. Through the study of social institutions, inequality, culture, and research design, they learn how sociologists frame questions and interpret data, while applying those same principles to their own research projects.
The year follows the complete data science process:
Ask Questions → Gather Data → Model → Analyze → Communicate
Throughout the course, students connect theory to action. They design surveys, analyze public datasets, critique how algorithms interpret human behavior, and apply ethical reasoning to data-driven decisions. The final Data Science Project brings it all together as students use AI tools to explore a sociological question of their choice.
Mathematics is not owned by traditional math courses. In this lab, it becomes something living—applied, interpretive, and deeply human. Here, numbers tell stories, data reveals systems, and students learn to see the world with both scientific precision and social understanding.
For readers interested in how Social Data Science aligns with statewide education goals and early college credit opportunities, you can review the full policy analysis:
For those interested in the broader policy implications of this course, you can review the full analysis here: Brief Policy Report: CLEP Sociology and Louisiana School Performance Score Alignment . This report, authored by Sean Muggivan (November 2025), examines how the Social Data Science course aligns with Louisiana’s BESE and LDOE accountability frameworks. It argues that reflective, data-informed innovation can turn existing policy structures into engines for genuine educational equity and early-college opportunity.
The lab operates as a local micro-cloud — a decentralized AI network built entirely within the classroom. Every component of the system is designed to run offline or within a tightly controlled local network, giving students a real-world model of data science infrastructure that values privacy, transparency, and independence.
Each workstation runs AI models locally using Ollama and LM Studio, all connected through a secure LAN managed by NextDNS filtering and Cloudflare-based routing. The network functions like a miniature research cloud — fast, private, and capable of distributed inference without relying on commercial servers.
All coursework and coding projects are version-controlled through GitHub Classroom, where students learn professional workflows: committing, branching, documenting, and pushing updates to shared repositories. GitHub acts as both a classroom hub and a living portfolio — a record of how each student’s work evolves across units and projects.
The lab’s web ecosystem is hosted through Cloudflare Pages and Cloudflare Workers, which handle secure data storage, form submissions, and user authentication. Cloudflare makes it possible to scale classroom-built tools — like AI feedback systems and student dashboards — without exposing data to the open internet.
The complete environment blends open-source tools, modern hardware, and strong ethical design principles:
Together, these systems form a self-contained ecosystem — one that mirrors the infrastructure of professional AI research while staying grounded in classroom values of autonomy, experimentation, and local ownership of technology. It shows students that the future of computing doesn’t have to live in the cloud; it can live right here, in our lab.
The muggsofdatasci.org ecosystem serves as a working proof-of-concept: a living résumé demonstrating that meaningful, privacy-preserving AI education can exist entirely within a classroom network.
Seán Muggivan (Mr. Mugg) is the creator of the Muggs of Data Science lab. A lifelong resident of the Greater New Orleans area and graduate of East Jefferson High School, he studied Creative Writing at the University of New Orleans before earning his Master’s in Social Work from Louisiana State University.
Before teaching, he practiced social work primarily with adolescents — helping young people in New Orleans and Jefferson Parish connect with opportunities for growth and stability. His work focused on youth at risk of out-of-home placement, and he also served on mitigation teams as part of the defense of individuals facing first-degree murder charges.
Over the past decade, Mr. Mugg has taught at a variety of New Orleans schools, almost always in mathematics, across both middle and high school levels and within general and special education settings. His classroom blends inquiry, empathy, and rigor — a space where students learn to think critically about both data and the world around them.
His teaching philosophy emphasizes “scientist AI” — using AI as a thinking partner, not an answer machine — and the belief that local, offline AI empowers students to take ownership of their learning, creativity, and data. He sees data science not just as a technical skill, but as a new form of literacy that connects mathematics, story, and social understanding.
Above all, Mr. Mugg is a teacher first. Teaching is not a step toward something else — it is the destination. His work in the classroom is not a means to an end, but a lifelong practice of learning alongside students. He believes that true innovation in education does not come from outside the classroom but from within it — from teachers and students building, questioning, and discovering together.
Every day, his students shape the curriculum as much as they follow it. Each lesson is a dialogue, an experiment, and a shared act of imagination. Mr. Mugg truly believes that he gets as much from his students as they, hopefully, get from him. His desire to remain a teacher is driven by the truth that in the classroom, among the teachers and students trying to make sense of the world together, is where true innovation happens.
The idea for Social Data Science grew out of real-world experience using AI to analyze complex, human-centered data. Before building this course, Mr. Mugg had the opportunity to complete a number of AI-assisted analytic projects involving qualitative datasets — including focus groups, open-ended survey responses, and written reflections from applicants.
Through that work, he saw how large language models (LLMs) could illuminate themes that traditional analysis might overlook — helping researchers identify patterns, tone, and sentiment across hundreds of responses without losing the voice of the participants. The technology wasn’t replacing the human perspective; it was amplifying it.
Coming from a background in social work and the social sciences, Mr. Mugg recognized that these same AI techniques could transform how students learn to read, interpret, and communicate about human systems. The goal was never to teach AI as a gadget or trend, but to show how it can be used immediately and meaningfully in the work of understanding people and society.
Social Data Science is the result of that realization — a course where students learn not just how AI works, but how it can help them think more deeply about the data that tells our collective story.