Most CS graduates can define abstraction. Few can tell you why they're doing it, or for whom. The same gap runs through OOP, SOLID, MVVM, architecture, testing, system design, AcharyaX closes for you. Twelve months, practitioner-led, from shell commands to shipping production-grade AI.
Engineering colleges teach what software is. Industry needs engineers who can build it. This course is designed to produce the second, taught by someone who's spent 15 years doing exactly that, taking products from 0 to 1, then from 1 to 10, across multiple startups.
Students graduate having written a hundred isolated assignments and zero real applications. They've memorized OOP definitions but never refactored production code. They know the words "system design" but have never made a single trade-off. The gap between graduation and being useful to a tech team is measured in months, not weeks.
The Android app a student builds in Month 5 gets refactored into clean architecture in Month 6, tested in Month 7, connected to a backend in Month 8, and shipped to production with AI features and guardrails in Month 12. They don't leave with knowledge, they leave with a product.
Most college syllabi stay frozen for a decade. The world doesn't work that way anymore. This course is engineered around two halves, a timeless foundation, and a constantly-refreshed industry edge.
The craft of software engineering, how to think, structure, test, and scale, is durable. These modules will be as relevant in 2034 as they are today. Once taught, they compound for a student's entire career.
The AI half of Phase 4 is updated every semester, new models, new tooling, new agent patterns, new failure modes from production. Only a practitioner embedded in industry can keep this current. This year's syllabus is genuinely different from last year's.
Each phase opens a new layer of engineering maturity. The work from one phase becomes the foundation for the next.
Click any module to see the breakdown, what's taught, what students build, what they walk away with.
Strip away the magic. Students leave knowing what actually happens between hitting Enter and a webpage loading.
No engineer ships alone. Students learn Git the way professionals actually use it, and how to work in a team without breaking each other's code.
Object-oriented thinking, taught in the language students will use for Android. Code is read more than it's written, this is where that habit forms.
Principles only stick when students see the pain they prevent. Every concept is taught through a before / after refactor of real code.
From a blank Android Studio project to a working multi-screen app. Modern Android, Jetpack Compose, declarative UI, state-driven thinking.
Why MVC evolved into MVP, then MVVM, then Clean Architecture. Same app, refactored four times, students feel the trade-offs in their fingers.
Untested code is code you don't actually understand. Students learn the test pyramid, write tests as they build, and ship at least one feature TDD-first.
The app needs a brain on the other side of the network. Students build a real backend the Android app actually talks to.
What happens when 100 users becomes 1 million? Students learn to reason about trade-offs the way senior engineers do, out loud, on a whiteboard.
AI is not a toy. It's a multiplier, but only for engineers who know how to wield it. Students learn the craft of working with AI as a co-developer.
From using AI to building with AI. Students learn how LLM APIs actually work, how to ground them in real data, and how to give them the ability to act.
Everything from the first eleven months, deployed and observed. Students don't graduate with knowledge, they graduate with a live product and a portfolio piece.
A live, AI-powered Android app with a working backend, deployed to production with monitoring and guardrails.
The same codebase, evolved through clean code, design patterns, MVVM, and Clean Architecture. Trade-offs felt firsthand.
Defended a system design for 1M users in a live whiteboard session. Speaks the language of caching, queues, and consistency.
Integrated LLMs, RAG, and tool use into a working application. Knows where AI breaks and how to defend against it.
Reviewed PRs, resolved merge conflicts, contributed to open source, written tests others depend on.
A live product, a public GitHub repo, a system design write-up, and a deployment story. Material a recruiter cannot ignore.
A great course is necessary but not sufficient. The deeper value to the department comes from how a Professor of Practice contributes outside the four walls of the classroom.
Students get to work on problem statements drawn from the actual world, not contrived textbook problems. They build things people would pay for.
I work alongside junior faculty interested in industry-aligned teaching, sharing how engineering teams actually work, how to design assignments that mirror real tasks, and how to evaluate student work the way industry would.
I'm available to advise the department on broader CS curriculum direction, what to add, what to retire, where to push deeper to stay aligned with where the industry is moving.
If your department is looking for a Professor of Practice who can take CS students from theory to shipping, let's start a conversation.