/computer-science-map v2

Awesome project — building a **Knowledge Vault for all of Computer Science** (with a focus on **web, cloud, DevOps, full‑stack, and cross‑platform native apps**) is absolutely doable if you use the right “maps” to discover what you don’t know yet.

Below is a structured, practical guide to help you surface **topics, concepts, and keywords you haven’t come across yet**, along with specific, high‑quality resources to mine for them.

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# 🚀 How to Discover “What You Don’t Know Yet”

## 1️⃣ Use established **Computer Science taxonomies**

These are curated, hierarchical maps of the field.

### **ACM Computing Classification System (ACM CCS)**

*   A definitive taxonomy used by researchers.
*   Topics span: Software engineering, networks, web, distributed systems, security, AI, theory, etc.

🔗 <https://dl.acm.org/ccs>

This alone can give you **hundreds of categories** and **thousands of keywords**.

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## 2️⃣ Mine course catalogs from top universities

They’re structured to cover entire domains and clearly reveal missing areas.

### Look at:

*   **MIT – EECS Course Map**  
    <https://www.eecs.mit.edu/academics-admissions/subjects>
*   **Stanford CS Course Catalog**  
    <https://explorecourses.stanford.edu>
*   **CMU Computer Science Curriculum**  
    <https://csd.cmu.edu/academics/courses>

How to use these:

*   Extract names & descriptions of courses.
*   Cluster them into domains (e.g., Operating Systems, Distributed Systems, Compilers, ML, Networks).

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## 3️⃣ Explore curated developer roadmaps

These roadmaps are excellent for **web, cloud, fullstack, DevOps**, etc.

### **Developer Roadmap**

> The most complete roadmap resource for the modern software stack.  
> <https://roadmap.sh>

Roadmaps you will want:

*   Frontend
*   Backend
*   Fullstack
*   DevOps
*   Cloud
*   Software Architecture
*   Docker
*   Kubernetes
*   React / Angular / Vue / Svelte
*   Android / iOS
*   Cross-platform (Flutter, React Native, .NET MAUI)

Each contains dozens of skills & buzzwords.

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## 4️⃣ Read professional certification outlines

They clearly enumerate **industry knowledge domains**.

### DevOps & Cloud

*   **AWS certification blueprints**
    *   Practitioner → Solutions Architect → DevOps Engineer  
        <https://aws.amazon.com/certification>
*   **Azure certification paths**  
    <https://learn.microsoft.com/en-us/certifications>
*   **Google Cloud certifications**  
    <https://cloud.google.com/certification>

### Security

*   **CompTIA Security+ Outline**
*   **CISSP Domain List**

These contain hundreds of topics you might not even know exist.

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## 5️⃣ Analyze “skill matrices” from job platforms

Job‑site aggregated skill graphs reveal market-required competencies.

### Good sources:

*   LinkedIn Skills Graph
*   Indeed "Skills" filter
*   O\*NET Skills Database (US Dept. of Labor)  
    <https://www.onetonline.org>
*   HackerRank skill taxonomies
*   StackOverflow 2024/2025 Developer Survey

These give you:

*   Current tech trends
*   Most required libraries, tools, languages
*   Emerging gaps in your knowledge

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## 6️⃣ Use topic graphs from MOOCs & learning platforms

MOOC platforms structure learning paths top‑down.

### Core platforms to mine:

*   Coursera Specializations (Cloud, ML, DevOps, Systems)
*   edX MicroMasters (Cloud Computing, CS Fundamentals)
*   Udacity Nanodegrees (DevOps, Cloud, Fullstack)
*   Pluralsight Paths (incredible taxonomy)
*   freeCodeCamp Curriculum

These breakdowns are fantastic for keyword extraction:

*   "Networking → Routing → BGP → Autonomous Systems → CIDR"
*   "DevOps → CI → CD → IaC → GitOps → Argo → Helm"

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## 7️⃣ Scan system‑design + engineering interview repositories

They list the most critical backend, distributed systems, and architecture topics.

### Best resources:

*   **System Design Primer (GitHub)**
*   **ByteByteGo**
*   **Grokking System Design**
*   **FAANG interview guides**

These include:

*   Distributed systems patterns
*   Scaling concepts
*   Deep backend knowledge
*   Message queues, caching, CAP theorem, consensus, etc.

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## 8️⃣ Explore curated “awesome lists” on GitHub

GitHub’s “awesome” lists are a goldmine for discovering entire topic ecosystems.

Start with:

*   <https://github.com/sindresorhus/awesome>
    Then branch into:
*   awesome-computer-science
*   awesome-cloud
*   awesome-devops
*   awesome-webdev
*   awesome-react / awesome-angular / awesome-vue
*   awesome-kubernetes
*   awesome-flutter
*   awesome-.NET / awesome-MAUI

These lists will expose you to an absurd breadth of unknown topics.

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## 9️⃣ Build a keyword extraction workflow

Since you're building a **knowledge vault**, automate discovering missing concepts:

### Feed these sources into a system:

*   Roadmaps
*   Course catalogs
*   Cloud certification outlines
*   GitHub awesome lists
*   FreeCodeCamp / MDN docs
*   StackOverflow tags
*   AWS / Azure / GCP documentation TOCs

Then:

1.  Extract keywords
2.  Cluster them into domains
3.  Create a dependency tree
4.  Rate your familiarity
5.  Identify gaps

I can help generate this structure **automatically** in future steps.

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# 🧠 A Suggested Starting Structure for Your Knowledge Vault

Here’s a high-level "universal taxonomy" covering your focus areas:

## **1. Foundations**

*   Algorithms & Data Structures
*   Networking
*   Operating Systems
*   Databases
*   Languages & Compilers

## **2. Web Development**

*   Frontend
*   Backend
*   APIs (REST / GraphQL)
*   Security
*   Performance

## **3. Cloud Computing**

*   AWS / Azure / GCP
*   Virtualization & Containers
*   Serverless
*   Cloud Networking
*   Observability

## **4. DevOps / SRE**

*   CI/CD
*   Infrastructure as Code (Terraform, Pulumi)
*   GitOps
*   Kubernetes
*   Monitoring & Logging

## **5. Software Architecture**

*   Patterns
*   Distributed Systems
*   Microservices
*   Event-driven systems
*   System Design

## **6. Cross-platform Native Apps**

*   Flutter
*   React Native
*   .NET MAUI
*   Kotlin Multiplatform
*   Capacitor / Tauri