📊 42 Python for Data Science Piscine

📚 Overview

The Python for Data Science Piscine project, is a collection of progressive modules to get familiar with the tools needed in AI/Data science... Let's dive in! 🌟

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🎯 Detailed Module Breakdown

Module 0: Starting

Core Concepts:

• Python basics and syntax fundamentals 🐍
• Data types and structures 🔢
• Script creation and function implementation 🛠️
• Error handling and argument parsing ⚠️

Key Topics:

• Lists, tuples, sets, and dictionaries
• String manipulation and formatting
• Type checking and NULL handling
• Command-line arguments
• Basic package creation
• Basic error handling
• First standalone programs

Keywords:

• Arguments handling
• Package management
• Error assertions
• Main function structure

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Module 1: Array

Core Concepts:

• Array manipulation and operations 📊
• Image processing fundamentals 🖼️
• NumPy array operations 🔢
• Basic data visualization 📉

Key Topics:

• 2D array operations
• Image loading and manipulation
• RGB color handling
• Array slicing and transformation
• Basic image filters
• Data visualization foundations

Keywords:

• NumPy arrays
• Image processing
• RGB channels
• Array slicing
• Matrix operations
• Data visualization
• Pixel manipulation

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Module 2: DataTable

Core Concepts:

• Dataset loading and manipulation 📂
• Data visualization techniques 📉
• Statistical analysis 📊
• Real-world data handling 🌍

Key Topics:

• CSV file handling
• Data visualization with matplotlib
• Country-specific data analysis
• Population data analysis
• Life expectancy analysis
• Data correlation studies

Keywords:

• Pandas DataFrame
• Data visualization
• Statistical analysis
• CSV manipulation
• Time series
• Data correlation
• Matplotlib

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Module 3: Object-Oriented Programming

Core Concepts:

• OOP principles and implementation 🧑‍💻
• Class inheritance and abstraction 🏰
• Method decorators 🎨
• Property management 🏡

Key Topics:

• Abstract class creation
• Multiple inheritance
• Class properties and methods
• Vector calculations
• Advanced class design patterns

Keywords:

• Classes
• Inheritance
• Abstract methods
• Properties
• Decorators
• Method overriding
• Vector operations

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Module 4: Data-Oriented Design (DOD)

Core Concepts:

• Advanced data structures 🏗️
• Performance optimization ⚡
• Functional programming concepts 🔄
• Design patterns 🧩

Key Topics:

• Statistics calculation
• Function decorators
• Data class implementation
• Inner/Outer functions
• Call limiting patterns

Keywords:

• Data classes
• Decorators
• Statistics
• Function wrappers
• Design patterns
• Performance optimization
• Functional programming

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