Part 1 · Coordinate System: Why I Recommend Non-CS People Start With This Stanford CS Framework

1. Where’s Your Ceiling?

Let me be blunt: people who can use AI tools are everywhere now.

I can write copy with ChatGPT, write small scripts with Claude, chain various agents into a pipeline—I do all of this, and I do it proficiently. I’ve built my own model proxy stack, run dozens of automation projects, and wield these tools more deftly than most programmers.

But one day I discovered something: being good at using tools doesn’t mean you can build them.

When I wanted to go from “calling a pre-made model API” to “training my own model,” from “running someone else’s open-source project” to “understanding why it was written that way,” from “the script works” to “it runs fast and runs reliably”—I hit a wall. This wasn’t a barrier posed by any specific tool, but a systematic knowledge barrier.

Concretely, these are the questions I couldn’t answer: What happens between source code and execution? Why do data structures have the complexity they do? When should an algorithm use dynamic programming? What role do mathematics like linear algebra and probability actually play in machine learning?

My go-to way of dodging these questions was “look it up when needed.” Look it up once, learn a little, forget a little. Knowledge was like scattered beads on the floor—each one useful, but impossible to string together.

Until I realized: self-learners don’t lack courses; they lack a coordinate system.

2. Why a “Curriculum”

The word “curriculum” sounds bureaucratic, like one of those nobody-reads forms pinned to the registrar’s office wall. But look at it from another angle: it’s a knowledge map forged over decades and generations by a university.

Why each course exists, why it’s placed where it is, why it carries these credits and not those—every decision rests on carefully considered dependencies: you must learn A before B, and B paves the way for C.

The most common mistake self-learners make is navigating without this map. Today you read an article about neural networks and think it’s cool, so you spend two days on it. Tomorrow you see a video on operating systems and think “this isn’t urgent,” so you skip it. The next day you want to learn algorithms, realize your math foundation is weak, and go back to fill the gap. After months of jumping back and forth, your output is zero—because learning without a dependency structure means you’re acquiring isolated facts that can never connect to each other.

Borrowing the skeleton of a mature curriculum is equivalent to standing on someone else’s shoulders. You don’t need to reinvent “what to learn first”; “what comes next” emerges naturally—because every course points to the next one.

That’s why I ultimately chose Stanford’s CS (Bachelor of Science in Computer Science) undergraduate curriculum, rather than cobbling together my own “AI crash course list.”

3. Why CS, Not “AI Applications,” Not Pure “Data Science”

I seriously considered the other two paths first, so let me explain why I ruled them out:

“AI Application Crash Course”: Various bootcamps and paid courses teach you to call libraries, run demos, and integrate APIs within days. After finishing, you can indeed build something—but the ceiling is obvious. You’re learning “how to use,” not “how to build.” When tools upgrade, libraries change, or you need to build something with no existing library, you’re back to zero. This isn’t learning; it’s consumption.

Pure “Data Science” path: People with my statistics/data analysis background are naturally drawn here. But it has a problem: data science knowledge is like an “add-on layer”—it sits on top of computer science fundamentals but often doesn’t reinforce the foundation. You learn a bunch of pandas, SQL, and statistical tests, but have no idea how databases work under the hood or how programs get optimized, and you’re still helpless when real engineering problems arise. And for someone with an R foundation like me, the marginal returns of pure data science are diminishing—rehashing the known doesn’t grow capability.

Computer Science (CS): It is the foundation for all of the above. AI is a building erected on that foundation; data science is a decorative floor on top of it. CS teaches the core: programming (how to turn ideas into code), data structures and algorithms (how to write efficient code), computer systems (how code actually runs), and mathematics (the formal language behind intelligence).

In one sentence: learning AI applications teaches you to use a hammer; learning data science teaches you to measure a house; learning CS teaches you to build a house. And the special thing about Stanford CS is that its AI-track advanced courses (Machine Learning, NLP, CV) are themselves part of the CS curriculum—the foundation and the building share the same blueprint, so you don’t need to learn them separately.

4. What Exactly Does Stanford’s CS B.S. Teach?

First, the official structure: the Stanford CS bachelor’s degree requires 180 units total, with the CS major accounting for roughly 96 units, broken down as follows—

Math Foundations: 26 units — Calculus, Linear Algebra, Probability
Science Foundations: 11 units — Physics, etc.
Engineering Foundations: 10 units
CS Core, Depth, and Thesis: 43 units — This is the main body

For self-learners, all the science/humanities/writing unit requirements can be safely ignored. What truly matters is this skeleton:

Layer 1: Programming Intro — CS106A → CS106B A covers programming methodology (Python intro; switched from Java to Python in 2023), B covers programming abstraction (data structures + recursion). This is the entry point of the entire system, solving the “can write code” problem. Without this layer, everything above it is built on sand.

Layer 2: Math Foundation — Linear Algebra, Discrete Math (CS103), Probability (CS109) Almost every warning about self-learning saying “algorithms/ML are too hard” points to the math underneath, not the algorithms themselves. CS103 teaches discrete math (proofs, sets, graph theory, logic); CS109 teaches probability. Both courses have exceptionally high openness of materials and can be studied completely at home. Note: CS103 has no prerequisites—you don’t need to take it before the programming courses. It’s usually taken in parallel with CS106A/B, or slightly after, and even taking it after CS107 is fine. The real entry point is always CS106A first.

Layer 3: Systems Core — CS107 (Computer Organization and Systems), CS111 (Operating System Principles) This layer answers “how programs actually run”: memory, pointers, compilation, OS scheduling. Many people study programming for years and only at this layer truly understand “what happens to my code on the machine.” This is the watershed that separates “can write code” from “understands computers.” (Note: the older CS110 was retired in Spring 2022; the current OS course is CS111.)

Layer 4: Algorithms — CS161 (Algorithm Design and Analysis) Upgrades the tools from data structures into the ability to “design solutions for novel problems.” Big-O, divide-and-conquer, dynamic programming, graph algorithms—these are what interviews test and what determines whether your code is 10× faster or 100× faster in real engineering.

Layer 5: Depth Tracks — AI / Systems / Theory / HCI, etc. Stanford requires choosing one track for in-depth study (25+ units, roughly 7 courses). The flagship AI combination is: CS221 (Principles of AI) + CS229 (Machine Learning) + CS224N (Natural Language Processing) + CS231N (Computer Vision). These four courses form the world’s most prestigious AI open-course sequence. Self-learners shouldn’t贪多; mastering two or three of them is enough to carry an AI track.

Notice what this structure is teaching: it’s not teaching 7 isolated courses; it’s building a map—from “code” to “machine” to “algorithms” to “intelligence,” where each layer stands on the one below it. That’s the coordinate system.

5. Why Stanford—and Why It Must Be Paired with MIT OCW

I chose Stanford not for the brand, but for openness of courseware. If you’re self-learning, whether you can freely access complete course materials, videos, and assignments is a matter of life and death.

Stanford’s open course system (Stanford Engineering Everywhere, abbreviated SEE) makes the videos and courseware for core courses like CS106A/B, CS107, and CS229 directly available for download. CS224N and CS231N courseware PDFs are also publicly available on their websites, with assignments and past exams easily accessible. This level of generosity is unique among top global universities.

But there are gaps: some courses (like CS103, CS161, CS110) have their videos locked behind Stanford’s internal system, inaccessible from the public internet.

This is where the second card comes in: MIT OpenCourseWare (OCW). MIT’s teaching style complements Stanford’s, and their policy is “everything is open”—PDF lecture notes, assignments, and exams are all downloadable, and most come with videos. For example, if Stanford’s video for discrete math is inaccessible, use MIT’s 6.042J instead. For algorithms, use MIT 6.006 → 6.046J. For machine learning, use 6.036 as a foundation.

Stanford provides “the best courses”; MIT provides “courses you can download everything for.” Put them together and you have a completely gap-free free self-study map. No other combination in the world matches this.

6. What Does Finishing the AI Track Actually Mean?

Finish this skeleton, especially the AI-track courses, and your capability undergoes a qualitative shift.

CS229 will teach you to turn “machine learning” from black-box mysticism into derivable mathematics: where the formulas for linear regression, logistic regression, and neural networks come from, why they work—you’ll be able to derive them yourself. CS224N teaches you to use deep learning for language tasks; CS231N for vision tasks. By that point, you’re no longer “someone who calls other people’s models”—you’re someone who can read models, modify models, and train models from scratch.

For someone who’s spent years with AI tools, this is the leap from “user” to “author.” The difference is like: knowing how to take a photo with a smartphone versus being able to build a camera from scratch.

7. How Much Is This System Worth to Someone Who Wants to Monetize?

If you’re just studying for enrichment, anything goes. But you and I both care about monetization—and in that case, time is the most expensive capital. Every hour must be invested where the return is highest.

My judgment: for non-CS majors who want to monetize through technology, systematically studying CS core is the highest ROI time investment, for three reasons:

First, every course directly converts into capability. Finish CS106B and you can write a 500+ line tool; finish CS161 and you can pass algorithm interviews and write high-performance code; finish CS229 and you can build your own models. Not a single course is a “wasted credit.”

Second, it’s an amortizable asset. Crash-course knowledge depreciates in three months because tools change; CS core knowledge doesn’t depreciate in a decade because it’s fundamental law. You’re borrowing “capability you can use for the next ten years.”

Third, it elevates your market positioning. The market is flooded with “people who can use AI,” but scarce in “people who understand systems, can build tools, and also understand AI.” This skeleton moves you from a red ocean to a blue one.

8. The Endpoint: A Degree You Can Actually Cash In

If you just want to “learn things,” the map above is enough. But you might want a more concrete endpoint—a degree. In my plan, hanging at the end of this map is: Georgia Tech’s Online Master of Science in Computer Science (OMSCS).

Reasons for choosing it: First, affordable—the entire degree costs approximately $7,000–$9,500 USD at 2026 rates (30 credits × ~$227/credit + per-semester fees, total varies with pacing). It’s the most cost-effective top-tier CS master’s worldwide (a regular US CS master’s runs $50,000–$100,000). Second, part-time friendly: one or two courses per semester, finish in three to six years, without interrupting your income. Third, no GRE required.

But there are three things you must know upfront, or you’ll hit pitfalls:

1. Free self-study courses alone won’t make your application competitive. OMSCS is direct about this—“MOOCs alone rarely suffice unless you have a STEM undergraduate degree.” They want “objectively provable CS fundamentals”: programming, object-oriented programming, data structures, and algorithms. The official recommendation is two make-up paths: GT’s edX certification certificates (Python intro, Java OOP, DSA—with exams and certificates, roughly 4,000–5,000 RMB), or 2–4 credit-bearing courses at a US community college earning B or above.

2. Don’t take admission rates for granted. The overall admission rate is indeed in the 60–80% range—but that includes a large number of CS/engineering-background applicants. Non-CS backgrounds are evaluated case by case, with fiercer competition. Don’t interpret “high admission rate” as “easy to get into.”

3. International students face additional hard thresholds. TOEFL ≥ 100 or IELTS ≥ 7.0—miss this and you’re rejected outright. You must complete 2 foundational courses within 12 months of enrollment and earn B or above. Also, the fully online degree cannot be certified by China’s Ministry of Education Service Center for Academic Degrees—it’s not recognized for government positions, civil service exams, or academic title reviews in China, but is fully recognized by private enterprises, foreign companies, startups, and overseas institutions.

So the route remains three layers:

Free courses for knowledge, certification courses for credentials, OMSCS for the degree.

Free self-study courses (the full set in this article) are responsible for solidifying knowledge—the true foundation. GT certificates are responsible for “proving you can.” OMSCS is responsible for turning capability into a degree. All three lie on the same chain, aligned with this map.

9. Closing: The Coordinate System Matters More Than the Courses

Let me circle back to the opening line: the key to self-learning isn’t finding courses—it’s finding a coordinate system.

The value of the Stanford CS curriculum isn’t in “prestigious university courses”; it’s that it’s a map refined over decades, where every course points to the next. With this map, every step tells you where you are and where to go next. Without it, you can spend three years piecing things together and still be spinning your wheels.

In the next two parts, I’ll unfold this map:

Part II: The Courseware Map: A course-by-course breakdown of free resources for 12 core courses—official sites, slide decks, videos, textbooks, and MIT equivalents. All in one post.
Part III: The Roadmap: Who to learn first, who comes next, how much time to invest per course, where you’re most likely to get stuck, and what to use as a milestone for each completed course.

The map is laid out. Now it’s just a matter of following it.