• ✅ Fundamentals: variables, types, operators, control flow
• ✅ Functions, modules, error handling
• ✅ Object-Oriented Programming (classes, inheritance, polymorphism)
• ✅ Functional programming & design patterns

• ✅ NumPy — Matrix operations, linear algebra
• ✅ Pandas — Data cleaning, aggregation, pivot tables
• ✅ Matplotlib — Visualization (charts, plots, dashboards)

• ✅ Flask — REST APIs, web applications
• ✅ Tkinter — Desktop GUI applications
• ✅ HTML/CSS integration for interactive web apps

• ✅ Groq & OpenAI API Integration
• ✅ Machine Learning — Classification & Logistic Regression with scikit-learn
• ✅ Automation scripts with PyAutoGUI

• ✅ Git & GitHub workflow (branches, PRs, commits)
• ✅ Clean code: PEP 8, naming conventions, docstrings
• ✅ Logging, testing, debugging strategies
• ✅ Virtual environments & dependency management
• ✅ Production-ready code quality

We teach Python the way professional engineers actually build systems:

1. 🏗️ Create — Build working code to understand concepts
2. 💥 Break — Intentionally introduce bugs to learn from failures
3. 🔧 Fix — Debug systematically using professional techniques
4. 💪 Harden — Add tests, docs, and best practices

Why? Because breaking things (safely) is how you truly learn to build them right.

• Python 3.8 or higher (Download)
• Git (Download)
• A code editor (VS Code recommended)

# Clone the repository
git clone https://github.com/QusaiALBahri/python-engineering-bootcamp.git
cd python-engineering-bootcamp

# Create virtual environment
python -m venv .venv

# Activate (Windows)
.venv\Scripts\activate
# OR activate (macOS/Linux)
source .venv/bin/activate

# Install dependencies
pip install -U pip
pip install -r requirements.txt

# Run a sample
python samples/hello.py

python_training/
├── day01_basics/                 ← Start here! 👈
│   ├── README.md                 (Lesson overview)
│   ├── 01_hello.py               (Lesson 1)
│   ├── 02_variables.py           (Lesson 2)
│   ├── 03_strings_fstrings.py    (Lesson 3)
│   ├── EXERCISES.md              (5-8 practice problems)
│   └── SOLUTIONS.md              (Complete solutions)
├── day02_logic_functions/
├── day03_loops_structures/
├── ... (days 4-14 follow same structure)
├── samples/                      ← Quick examples
│   ├── hello.py
│   ├── discount.py
│   ├── grades_miniclass.py
│   └── ... (8 demo scripts)
├── docs/                         ← Documentation
│   └── client_proof/             (Verification files)
├── .env.example                  (API keys template)
├── requirements.txt              (All dependencies)
└── README.md                     (This file)

📖 README.md — Topic overview & learning outcomes
📝 Lesson Files — 2-4 runnable, well-commented lessons
🏋️ EXERCISES.md — 5-10 practice problems
✅ SOLUTIONS.md — Complete code solutions with explanations

# Learn: input, f-strings, type checking
name = input("What's your name? ")
age = int(input("Your age: "))
print(f"Hello, {name}! Next year you'll be {age + 1}.")
print(f"Type check - name is {type(name).__name__}, age is {type(age).__name__}")

# Learn: Pandas, Matplotlib, creating charts
import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    "city": ["Amman", "Zarqa", "Irbid"],
    "revenue": [45000, 32000, 28000]
})

df.plot(x="city", y="revenue", kind="bar", title="Revenue by City")
plt.savefig("revenue_chart.png")
plt.show()

# Learn: Flask, JSON APIs, HTTP methods
from flask import Flask, jsonify, request

app = Flask(__name__)

@app.route('/api/greet', methods=['POST'])
def greet():
    name = request.json.get('name')
    return jsonify({"message": f"Hello, {name}!"})

if __name__ == '__main__':
    app.run(debug=True)

# Learn: scikit-learn, model training, evaluation
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LogisticRegression().fit(X_train, y_train)
accuracy = accuracy_score(y_test, model.predict(X_test))
print(f"✅ Model Accuracy: {accuracy:.2%}")

• Master variables, types, control flow
• Write your first functions
• Understand lists, loops, and conditionals
• Outcome: Build a simple calculator

• Learn professional debugging techniques
• Master Object-Oriented Programming
• Follow PEP 8 standards
• Work with Git and GitHub
• Outcome: Clean, maintainable code

• Analyze data with Pandas & NumPy
• Create visualizations with Matplotlib
• Build web APIs with Flask
• Design desktop GUIs with Tkinter
• Outcome: A working dashboard

• Integrate with AI APIs (OpenAI, Groq)
• Build an AI chatbot
• Learn Machine Learning basics
• Deploy automation scripts
• Outcome: An AI-powered application

• Python Docs — Core language reference
• NumPy Guide — Array operations
• Pandas Guide — Data manipulation
• Flask Guide — Web framework
• scikit-learn Guide — Machine learning

• Automate the Boring Stuff — Practical automation
• Real Python — In-depth tutorials
• W3Schools Python — Quick reference
• Hugging Face — AI models & datasets

• GitHub — Code hosting & collaboration
• VS Code — Code editor
• Groq — Free AI API (no charges)
• Google Colab — Free cloud notebooks

After completing this bootcamp, you can:

✅ Write clean, maintainable Python code following industry standards
✅ Build full-stack web applications with Flask + frontend
✅ Analyze data professionally using Pandas & Matplotlib
✅ Create desktop applications with graphical user interfaces
✅ Integrate AI APIs into your projects (OpenAI, Groq, etc.)
✅ Implement machine learning models with scikit-learn
✅ Collaborate effectively using Git and GitHub
✅ Debug systematically and approach problems methodically
✅ Deploy projects with professional documentation
✅ Contribute to open-source projects confidently

Days 6 & 8 include intentional bugs (tickets #701-705, #801-805) to teach professional debugging:

• Day 6: Fix GPA calculation system
• Day 8: Fix Agile sprint project issues

Learn to debug like a professional engineer! 🔍

50+ practice problems across all 14 days:

• Core concepts — Verify understanding of each lesson
• Progressive difficulty — From basic to advanced challenges
• Real-world scenarios — Business logic, data analysis, web apps
• Complete solutions — Compareé your code to professional implementations

Every exercise has:

• Clear requirements
• Expected output examples
• Hints for stuck learners
• Full code solutions

Found an issue or want to improve the course?

1. Fork the repository
2. Create a branch (git checkout -b feature/improvement)
3. Make your changes
4. Submit a Pull Request

All contributions welcome! ❤️

<<<<<<< HEAD

• Questions? Open an Issue
• Suggestions? Start a Discussion
• Email — qusai@albahri.org =======

Need help or have feedback? Multiple ways to reach out:

📧 Email: qusai@albahri.org
💼 LinkedIn: linkedin.com/in/qusai-albahri
📘 Facebook: facebook.com/qusai.albahri
🌐 Portfolio: albahri.org
🐙 GitHub Issues: Open an issue
💬 Discussions: Start a discussion

eaa5039 (Complete: Add 6 missing lesson files + beautify README)

This project is licensed under the MIT License — see LICENSE file for details.

If this bootcamp helped you learn Python, please:

⭐ Star this repository on GitHub
📢 Share with friends who want to learn Python
💬 Leave feedback in the discussions
🔗 Mention in your portfolio

git clone https://github.com/QusaiALBahri/python-engineering-bootcamp.git
cd python-engineering-bootcamp
python samples/hello.py