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Python Essentials

Interactive Learning Platform

A comprehensive 11-week journey from Python fundamentals to web development and machine learning. Each week includes interactive HTML lessons, hands-on Jupyter notebooks, and group activities. Track your progress, practice in notebooks, and build real-world projects!

GitHub Repo Contribute Setup Guide

How to Use This Curriculum

1. Read the Lesson: Start with the interactive HTML lesson for theory and examples.
2. Practice in Notebooks: Download or open notebooks in Colab for hands-on coding.
3. Group Activities: Complete session 2 notebooks with peers for collaborative learning.
4. Track Progress: Mark weeks complete to unlock achievements and track your journey!

Getting Started with Jupyter Notebooks

Google Colab (Easiest)
Local Setup
VS Code

Google Colab (Recommended for Beginners)

  1. Click any Colab button on a week card below
  2. Sign in with your Google account (free)
  3. The notebook opens ready to run in your browser
  4. Click the play button (▶) next to any code cell to run it
  5. Changes auto-save to your Google Drive

Pros: No installation, runs in browser, free GPU access, auto-saves
Note: Requires internet connection

Local Jupyter Setup

  1. Install Python 3.8+ from python.org
  2. Open terminal/command prompt
  3. Install Jupyter: pip install jupyter notebook pandas matplotlib
  4. Clone/download this repo: git clone https://github.com/StrayDogSyn/Python-Essentials-Code-The-Dream.git
  5. Navigate to repo: cd CTD
  6. Start Jupyter: jupyter notebook
  7. Browser opens automatically - navigate to notebook-sessions/

Pros: Works offline, faster execution, full control
Requires: Python installation, ~500MB disk space

VS Code Jupyter Extension

  1. Install VS Code
  2. Install Python extension in VS Code
  3. Install Jupyter extension in VS Code
  4. Install Python 3.8+ and pip
  5. Open repo folder in VS Code
  6. Click any .ipynb file in notebook-sessions/
  7. Select Python kernel when prompted
  8. Run cells with Shift+Enter

Pros: Professional IDE, debugging, Git integration
Best for: Developers comfortable with IDEs

Week 1
Introduction to Python
Environment setup • Variables & data types • Control flow • Functions • Debugging
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 2
Data Structures & File Handling
Lists • Dictionaries • Tuples & Sets • File I/O • CSV files • Modules
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 3
Advanced Python Skills
Object-Oriented Programming • Decorators • List comprehensions • Closures
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 4
Data Engineering Fundamentals
Pandas basics • Series & DataFrames • ETL patterns • Data inspection
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 5
Data Wrangling & Aggregation
GroupBy operations • Pivot tables • Merging datasets • Aggregation functions • Data transformation
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 6
Data Cleaning & Validation
Data quality • Missing values • Outliers • Validation techniques • Data profiling
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 7
Advanced Data Cleaning
Regex patterns • String methods • Fuzzy matching • GroupBy transform • Complex cleaning
Start Lesson →
S1 Solo Practice
Week 8
Databases & SQL
SQL fundamentals • Queries • Joins • Database design • Python integration
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 9
Introduction to Machine Learning
Scikit-Learn basics • Regression • Classification • Model evaluation • Predictions
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 10
Flask Web Development
Flask basics • Routes • Templates • Forms • REST APIs • Web applications
Start Lesson →
S1 Solo Practice
S2 Group Session
Week 11
Deployment & Final Projects
Deployment strategies • Cloud hosting • CI/CD • Project showcase • Best practices
Start Lesson →
S1 Solo Practice
S2 Group Session