Welcome to Python Training - A comprehensive, production-ready Python learning program designed to take you from absolute beginner to proficient developer.
Status: ✅ Complete - All 14 days created with lesson content, exercises, and professional documentation
- Overview | نظرة عامة
- Roadmap (Sessions) | خارطة الطريق (الجلسات)
- Quick Start | البداية السريعة
- Directory Layout | هيكلية المجلدات
- Code Samples | أمثلة برمجية
- Tickets & Workflow | التذاكر وتدفق العمل
- Deliverables | المخرجات
- Resources | الموارد
- Total Sessions | عدد الجلسات: 14 (كل جلسة ٣ ساعات، الأخيرة ساعتان)
- Approach | المنهجية: أنشئ → اكسر → أصلح → حصّن.
- Mindset | الفكر: برمجة بمستوى هندسي: PEP 8، تسجيل (logging)، توثيق، GitHub.
- Outcomes | النتائج: كتابة كود نظيف، مهارات تحليل البيانات، تطبيقات GUI/ويب، أتمتة ودمج مع الذكاء الاصطناعي.
| # | Hours ⏱️ | Focus 🎯 | What You’ll Do 🧩 | المحاور |
|---|---|---|---|---|
| 1 | 3h | Basics: Print/Input, Variables, Data Types | Hello + Name, Age next year | الأساسيات: إدخال/إخراج، المتغيرات، الأنواع |
| 2 | 3h | Functions, Errors, Conditionals, Logic | Discount calc, Login system | الدوال، الأخطاء، الشروط |
| 3 | 3h | Loops, Lists, OOP Intro | Dice roll, Grades tracker | الحلقات والقوائم ومقدمة OOP |
| 4 | 3h | List Comp, Files, Decorators, venv | Notes saver, One-liners | القوائم المركبة، الملفات، المولدات |
| 5 | 3h | Debugging & Workflow | Shopify bug simulation | التصحيح وتدفق العمل |
| 6 | 3h | Systems Eng — GPA System | Fix bugs #701–705 | هندسة الأنظمة — نظام علامات |
| 7 | 3h | OOP Advanced, Refactor | Clean intern code | OOP متقدم وإعادة هيكلة |
| 8 | 3h | Agile Sprints + Libraries | Mini projects per lib | سباقات أجايل + مكتبات |
| 9 | 3h | Data Analysis (pandas+matplotlib) | Clean CSVs + charts | تحليل البيانات + الرسوم |
| 10 | 3h | GUI (Tkinter) + Flask | Mini dashboard | تطبيقات رسومية وويب |
| 11 | 3h | Flask, PyAutoGUI, AI APIs | Build AI-powered apps | أتمتة وذكاء اصطناعي |
| 12 | 3h | Extra Resources | Self-learning toolkit | موارد إضافية للتعلم |
| 13 | 3h | Pandas + Colab Workshop | Business insights | ورشة تحليل بيانات |
| 14 | 2h | Logistic Regression (ML) | First ML algorithm | الانحدار اللوجستي |
# 1) Create & activate a virtual environment
python -m venv .venv
# 2) Activate
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
# 3) Install dependencies | تثبيت المكتبات
pip install numpy pandas matplotlib flask scikit-learn pyautogui faker duckdb colorama playsound
# 4) Run sample | تشغيل مثال
python samples/hello.py📦 python-training
├─ day01_basics/ # الأساسيات
├─ day02_logic/ # المنطق والدوال
├─ day03_loops/ # الحلقات والهياكل
├─ day04_advanced/ # متقدم
├─ day05_debugging/ # التصحيح
├─ day06_systems/ # الأنظمة
├─ day07_refactor/ # إعادة هيكلة
├─ day08_agile/ # أجايل والمكتبات
├─ day09_data_viz/ # تحليل البيانات
├─ day10_gui/ # واجهات رسومية
├─ day11_web_ai/ # ويب + ذكاء اصطناعي
├─ day12_resources/ # موارد إضافية
├─ day13_colab/ # ورشة Colab
├─ day14_ml/ # التعلم الآلي
└─ samples/ # أمثلة سريعة
name = input("اسمك: ")
age = int(input("عمرك: "))
print(f"مرحبًا {name}! العام القادم سيكون عمرك {age + 1}")grades = [90, 80, 70]
gpa = sum(grades) / len(grades)
print("المعدل:", gpa)- 701–705: مشاكل في نظام GPA (أخطاء منطقية، ملفات ناقصة، تسجيل ضعيف).
- 801–805: مشاكل في مشاريع أجايل (أخطاء في البريد الإلكتروني، البيانات المزيفة، إدخال قاعدة البيانات).
✅ الحلول عبر فروع GitHub: naive → hardened.
✅ توثيق العميل في docs/client_proof/.
- CSVs نظيفة + رسوم بيانية
- تطبيقات صغيرة (GUI + Flask)
- فروع naive/hardened
- ملفات توثيق + كود نظيف
- Python Docs — وثائق بايثون
- Automate the Boring Stuff — أتمتة المهام المملة
- Hugging Face — منصة الذكاء الاصطناعي
- W3Schools Python — شرح بايثون
- Pandas — تحليل البيانات
- Matplotlib — الرسوم البيانية
Choose a license (MIT, Apache, CC-BY).
اختر رخصة مناسبة: MIT أو Apache أو CC-BY.
Hands-on. Production-minded. Fun.
This repository powers a complete Python training path: from basics and logic to OOP, data analysis, GUI/web, automation, and AI — with real deliverables, ticket-driven debugging, and a professional GitHub workflow.
- Overview
- Roadmap (Sessions)
- Quick Start
- Directory Layout
- Code Samples
- Tickets & Professional Workflow
- Deliverables
- Resources
- Total Sessions: 14 (each ~3h, last is 2h)
- Approach: Create → Break → Fix → Harden.
- Mindset: Write like an engineer: PEP 8, logging, tests, client proof, GitHub discipline.
- Outcomes: Clean code, data skills, GUI/web apps, automation, and AI integration.
| # | Hours | Focus | What You’ll Do |
|---|---|---|---|
| 1 | 3h | Print/Input, Comments, Variables, Operators, Data Types, Strings, Type Conversion, f-Strings | Playful CLI mini-exercises (Hello + Name, Age next year). Reinforce type() early. |
| 2 | 3h | Maths & Modulo, Errors, Functions, Conditionals, Logical Operators | Discount calculator, login check. Trigger errors (e.g., 10/0) → catch & fix. |
| 3 | 3h | Loops, Loop Control, Lists, Built‑ins, Modules, OOP Intro, Dicts/Tuples/Sets, String Methods | Multiplication table, dice roll, student grades. One class with __init__ + one method. |
| 4 | 3h | List Comprehensions, Error/File Handling, Custom Modules, Advanced Functions, Decorators, Generators, Date/Time, venv, Pythonic shorthand | Notes/logs writer, one‑liners for data cleaning, concept demos for decorators/generators. |
| 5 | 3h | Professional Workflow — Debugging “Revenue Leaks”, PEP 8, formatting, naming, imports, client verification | Simulate production bug (Shopify‑style). Fix, document, and push to GitHub. |
| 6 | 3h | Systems Engineering — GPA Calculator, File Resilience, Dict vs List at scale, Logging, Refactoring | Build grade system → fix bugs (#701–705). Ship v1-naive & v2-hardened branches. |
| 7 | 3h | OOP Advanced, Data Structures at Scale, File Mastery, System Refactor | Senior‑engineer refactor of “intern code”. Emphasize auditability + docstrings. |
| 8 | 3h | Agile Sprints + Libraries: colorama, smtplib, faker, duckdb, playsound |
Five mini‑sprints with user stories + commits. Fix injected bugs (#801–805). |
| 9 | 3h | Data Analysis & Visualization (pandas, matplotlib, venv, git) | Clean sales.csv (NaNs/dupes), build 4 charts, add business notes. |
| 10 | 3h | Tkinter GUI — Widgets, Geometry (pack/grid/place), Events — Flask intro |
Build a mini dashboard (multiple widgets). Understand event loop & layouts. |
| 11 | 3h | Flask → PyAutoGUI → AI APIs (OpenAI/DeepSeek/Groq/Google) → Local AI (LM Studio) → HF usage | Integrate APIs, local models, and automation into apps (safe keys/config). |
| 12 | 3h | Extra Resources — Teachable Machine, Automate the Boring Stuff, Hugging Face, W3Schools | Curated self‑learning directions with practical next steps. |
| 13 | 3h | Pandas & Matplotlib (Colab) — Clean → Describe → Pivot/Group → Visualize | Non‑specialist, all‑in‑one workshop. CSV → insights → decisions. |
| 14 | 2h | Logistic Regression — Sigmoid, Decision Boundary, Cost, Gradient Descent, scikit‑learn | First ML algorithm. Intuition + plots + thresholding. |
# 1) Create & activate a virtual environment
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
# 2) Install core dependencies
pip install -U pip
pip install numpy pandas matplotlib duckdb faker colorama playsound flask pyautogui scikit-learn
# 3) (Optional) Save exact versions
pip freeze > requirements.txt
# 4) Run a sample
python samples/hello.pyNote:
tkinteris part of standard Python on most systems (on some Linux distros, installpython3-tk).
📦 python-training
├─ day01_basics/
├─ day02_logic_functions/
├─ day03_loops_structures/
├─ day04_power_user/
├─ day05_workflow_debugging/
├─ day06_systems_gpa/
├─ day07_refactor_oop/
├─ day08_agile_sprints_libs/
├─ day09_data_viz/
├─ day10_gui_tkinter/
├─ day11_web_flask_ai/
├─ day12_extra_resources/
├─ day13_colab_pandas_matplotlib/
├─ day14_logreg_ml/
└─ samples/
├─ hello.py
├─ discount.py
├─ grades_miniclass.py
├─ pandas_quickstart.py
├─ tkinter_min.py
├─ flask_min.py
└─ logreg_sklearn.py
# samples/hello.py
name = input("Your name: ").strip()
age = int(input("Your age: "))
print(f"Hello, {name}! Next year you'll be {age + 1}.")
print("Type is:", type(name).__name__)# samples/discount.py
price = float(input("Price: "))
code = input("Coupon code (VIP10/None): ").strip().upper()
try:
disc = 0.10 if code == "VIP10" else 0.0
total = price * (1 - disc)
print(f"Total: {total:.2f}")
# force an error to learn try/except
x = 10 / int(input("Divide 10 by: ")) # try 0
print("Result:", x)
except ZeroDivisionError:
print("⚠️ Can't divide by zero — fixed gracefully.")# samples/grades_miniclass.py
class Student:
def __init__(self, name, grades):
self.name = name
self.grades = grades
def average(self):
return sum(self.grades) / max(len(self.grades), 1)
s = Student("Lina", [80, 88, 92])
for g in s.grades:
print("Grade:", g)
print("Average:", s.average())# samples/pandas_quickstart.py (part 1 — no pandas yet)
lines = [" apple ", "banana", " cherry ", None, ""]
clean = [x.strip() for x in lines if x and x.strip()]
with open("notes.txt", "w", encoding="utf-8") as f:
f.write("\n".join(clean))
print("Saved notes.txt:", clean)# samples/pandas_quickstart.py (part 2)
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({
"city": ["Amman", "Zarqa", "Irbid", "Amman", "Zarqa"],
"revenue": [1200, 900, 700, 1500, 1100],
})
summary = df.groupby("city", as_index=False)["revenue"].sum()
print(summary)
summary.plot(kind="bar", x="city", y="revenue", title="Revenue by City")
plt.tight_layout()
plt.savefig("revenue_by_city.png")
print("Saved chart: revenue_by_city.png")# samples/tkinter_min.py
import tkinter as tk
root = tk.Tk()
root.title("Mini App")
tk.Label(root, text="Hello GUI").pack(padx=10, pady=10)
tk.Button(root, text="Close", command=root.destroy).pack(pady=5)
root.mainloop()# samples/flask_min.py
from flask import Flask, render_template_string
app = Flask(__name__)
@app.get("/")
def home():
return render_template_string("<h1>Hello, Flask!</h1>")
if __name__ == "__main__":
app.run(debug=True)# samples/logreg_sklearn.py
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
X, y = make_classification(n_samples=500, n_features=2, n_redundant=0,
n_informative=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
clf = LogisticRegression().fit(X_train, y_train)
pred = clf.predict(X_test)
print("Accuracy:", accuracy_score(y_test, pred))All samples are intentionally short and readable — perfect for classroom demos and quick labs.
Bug sets used in sessions:
- #701–705: GPA system issues (averaging bug, missing file resilience, wrong data shape, weak logging, non‑PEP8 names).
- #801–805: Agile sprints problems (bad CLI UX, flaky email sender, fake data skew, DB insert edge case, blocking audio call).
Proof & Discipline:
- Use branches:
v1-naive→v2-hardened - Open PRs with checklists: style, tests (if any), docs, before/after screenshots
- Drop “client verification” messages in
docs/client_proof/(text or markdown)
- ✅ Cleaned CSVs (e.g.,
sales_clean.csv) and exported charts (PNG/SVG) - ✅ Mini GUI app, minimal Flask app
- ✅
requirements.txtand venv hygiene - ✅ Branches for naive vs hardened implementations
- ✅ Clear README and sample scripts
- Python Docs — https://docs.python.org/3/
- Automate the Boring Stuff — https://automatetheboringstuff.com/
- Hugging Face — https://huggingface.co/
- W3Schools Python — https://www.w3schools.com/python/
- Pandas — https://pandas.pydata.org/
- Matplotlib — https://matplotlib.org/
Educational materials — choose a license that matches your goals (MIT, Apache‑2.0, CC‑BY‑SA).
