Add comprehensive data engineering learning path with Python and SQL curriculum

[Copilot]

Overview

This PR establishes a complete, structured learning path for aspiring data engineers, covering essential Python programming and SQL database skills. The repository now provides a comprehensive curriculum that takes learners from absolute beginners to job-ready data engineers.

What's Included

📚 Core Documentation

• README.md: Complete learning roadmap with 7 progressive phases covering Python fundamentals through advanced topics
• GETTING_STARTED.md: Detailed beginner's guide with week-by-week action plans, study routines, and setup instructions
• FAQ.md: Addresses 30+ common questions about learning, tools, career paths, and troubleshooting
• CONTRIBUTING.md: Guidelines for community contributions
• requirements.txt: All Python dependencies needed for the learning path

🎓 Learning Curriculum (7 Sections)

1. Python Fundamentals: Variables, control flow, functions, OOP, file I/O with working examples and 10 exercises
2. Python for Data Engineering: Pandas, data manipulation, file formats (CSV/JSON/Excel), API interactions
3. SQL Fundamentals: Query writing, joins, aggregations with practical SQL examples
4. Advanced SQL: Database design, normalization, indexes, window functions, optimization
5. Data Engineering Concepts: ETL/ELT processes, data pipelines, data quality, orchestration
6. Advanced Topics: Apache Spark, cloud platforms (AWS/GCP/Azure), Docker, testing, CI/CD
7. Capstone Projects: Three real-world projects including ETL pipeline, data warehouse, and real-time dashboard

💻 Practical Examples

• Python hello world and basic operations (tested and working)
• Pandas data manipulation examples with DataFrames, grouping, merging
• Complete ETL pipeline example with extract, transform, load phases
• SQL query examples covering SELECT, WHERE, JOINs, aggregations, window functions

📖 Learning Resources

• books.md: 15+ recommended books from beginner to advanced
• courses.md: Curated online courses, certifications, and practice platforms
• tools.md: Essential software setup guide (IDEs, databases, Docker, cloud platforms)
• cheatsheet.md: Quick reference for common Python/SQL operations, Git, and Docker commands

🎯 Key Features

• Progressive Learning: Structured path from basics to production-ready skills
• Time Estimates: Clear timelines (3-12 months depending on commitment)
• Hands-on Practice: Examples, exercises, and projects throughout
• Career Focused: Portfolio-building projects and interview preparation
• Self-Paced: Flexible learning with multiple study schedule options
• Community Ready: Contributing guidelines and discussion support

Repository Structure

├── README.md (main learning roadmap)
├── GETTING_STARTED.md
├── FAQ.md
├── CONTRIBUTING.md
├── LICENSE (MIT)
├── requirements.txt
├── 01-python-fundamentals/
├── 02-python-data-engineering/
├── 03-sql-fundamentals/
├── 04-advanced-sql/
├── 05-data-engineering/
├── 06-advanced-topics/
├── 07-projects/
└── resources/
    ├── books.md
    ├── courses.md
    ├── tools.md
    └── cheatsheet.md

Target Audience

• Aspiring data engineers with no prior experience
• Career changers looking to enter data engineering
• Software engineers wanting to specialize in data
• Students seeking structured self-study curriculum
• Anyone interested in learning Python and SQL for data work

Ready to Use

The repository is immediately usable. Learners can:

1. Clone the repository
2. Follow the GETTING_STARTED guide
3. Progress through sections sequentially
4. Complete exercises and projects
5. Build a portfolio of work

All code examples have been tested and work correctly. The structure allows for easy expansion with additional lessons, exercises, and solutions in the future.

Original prompt

data_engineer_learning_python_sql_path

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