The core Data Analytics learning track of the Akxyverse ecosystem.

Fundamentals → Programming → Data → BI Tools — structured, curated, always evolving.

🧭 What A curated, public record of my core Data Analytics learning — fundamentals through BI tooling.

💡 Why Skills scattered across notes apps disappear. Structured, versioned, public notes don't.

👥 Who Learners, recruiters, and hiring managers evaluating depth — not just finished projects.

🎁 Value A ready-made, freely reusable template for anyone building their own learning system.

🧭 Explore the Ecosystem

🏠 Profile

🧠 Knowledge Hub

🚀 Projects

📦 Datasets

📚 Resources

💼 Career

✍️ Content

🏅 Certs

🤖 AI Automation — the advanced/specialized track, kept separate from core analytics

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🎓 Curriculum

Two courses, taken in order — like a real academy track.

DA-101 🧮 Fundamentals of Data Analytics The theoretical foundation — no tools, just the math and stats that make everything else make sense. Syllabus: Linear Algebra · Calculus · Discrete Math · Descriptive & Inferential Statistics · Probability Learning outcome: Reason confidently about distributions, variance, and inference before ever touching a dataset. Status: ⚪ Not yet enrolled 📂 Open course  ·  📄 Syllabus

DA-201 🛠️ Data Analytics Technologies Every core tool used in day-to-day analytics — one home per topic, grouped by category. Syllabus: Python · SQL & Databases · Excel · Power BI · Tableau · Looker Studio · APIs · Git/GitHub Learning outcome: Move from raw data to a cleaned, queried, visualized, version-controlled analysis. Status: 🟢 In session — real notes already on file in Spreadsheets/Excel 📂 Open course  ·  📄 Syllabus

📁 Full folder tree

data-analytics-knowledge-system
│
├── Fundamentals of Data Analytics
│   ├── Mathematics (Linear Algebra, Calculus, Discrete Mathematics)
│   ├── Statistics (Descriptive Statistics, Inferential Statistics)
│   └── Probability
│
└── Data Analytics Technologies
    ├── Programming
    │   ├── Python (Python Fundamentals, Python Libraries: NumPy, Pandas,
    │   │   Matplotlib, Seaborn, Plotly, Polars, SciPy, Statsmodels, Requests, Others)
    │   └── Web Development (Streamlit)
    ├── Databases (SQL, MySQL, PostgreSQL, SQL Server, SQLite, MongoDB, Redis)
    ├── Spreadsheets (Excel)
    ├── Data Visualization (Power BI, Tableau, Looker Studio, Excel Dashboards)
    ├── Analytics Concepts (Data Analytics Concepts, Business Analytics, Data Storytelling)
    ├── APIs (REST APIs, GraphQL, Webhooks, Authentication)
    ├── Version Control (Git, GitHub)
    └── Other Tools

🗺 Learning Roadmap

1 Fundamentals → 2 Programming → 3 Data Handling → 4 Visualization → 5 🚀 Applied Projects

flowchart LR
    A["🧮 Fundamentals<br/>Math · Stats · Probability"] --> B["🐍 Programming<br/>Python · SQL"]
    B --> C["🗄️ Data Handling<br/>Databases · Spreadsheets"]
    C --> D["📊 Visualization<br/>Power BI · Tableau"]
    D --> E["🚀 Applied Projects<br/>data-analytics-projects"]

    style A fill:#0a1122,stroke:#38bdf8,color:#f8fafc
    style B fill:#0a1122,stroke:#38bdf8,color:#f8fafc
    style C fill:#0a1122,stroke:#22d3ee,color:#f8fafc
    style D fill:#0a1122,stroke:#22d3ee,color:#f8fafc
    style E fill:#0f4c4a,stroke:#5eead4,color:#f8fafc

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_{Text equivalent for screen readers: Fundamentals → Programming → Data Handling → Visualization → Applied Projects (in the dedicated projects repository).}

🧰 Tech Stack

Programming & Web Core Data Libraries

Databases Visualization & Tools

Looking for TensorFlow, PyTorch, Scikit-learn, Cloud, or Generative/Agentic AI? Those live in ai-automation or locally for now (ML/DL) — not here, by design.

📘 Recommended Guidebooks

Complete, self-contained learning guides — including the Excel Roadmap Guide — now live in Learning Resources → Guidebooks, so there's one home for them instead of two.

🎯 How to Use This Repository

1. Start with fundamentals — Fundamentals of Data Analytics if you're building from zero.
2. Move to tools — Data Analytics Technologies once the theory is solid.
3. Go apply it — real, hands-on work lives in data-analytics-projects, not here.

🟢 Status

Category	Status
Fundamentals of Data Analytics	⚪ Not Started
Data Analytics Technologies	⚪ Not Started

🟢 Active · 🟡 Planned · ⚪ Not Started · ✅ Complete

➡️ Recommended Next Repository

🚀 Data Analytics Projects

Fundamentals mean nothing until they're applied. This is where the theory here becomes real, hands-on work.

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⭐ Star this repo if the structure is useful to you.

_{Part of the Akxyverse Data Analytics ecosystem · MIT Licensed · LICENSE}