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Python Course Documentation (From Zero to Advanced)

Welcome to the Python Course!

This course is designed to take you from the most basic concepts of Python to the application of advanced techniques, turning you into a competent and capable programmer.

Why Learn Python?

Python has established itself as one of the most popular and versatile programming languages in the world for several reasons:

  • Clear and Readable Syntax: Python resembles natural language, making it easy to learn and read code, which is ideal for beginners.
  • Wide Range of Applications: From web development and data science to artificial intelligence and automation, Python has applications in virtually every field of technology.
  • Large Community and Resources: The vast Python community provides abundant documentation, tutorials, libraries, and frameworks, which facilitates problem-solving and collaboration.
  • High Salary and Job Demand: Python developers are in high demand in the industry, which translates into job opportunities and competitive salaries.
  • Ideal for Beginners and Experts: Whether you are an absolute beginner or an experienced programmer, Python allows you to develop projects efficiently and scalably.

What Will You Learn in This Course?

This course will guide you step by step through all the fundamental and advanced concepts of Python. Here is a general structure of what we will cover:

🚀 Introduction to Python

What is Python and Why Use It?

  • History and philosophy of Python
  • Python 2 vs Python 3
  • Python ecosystem and community

Environment Setup

  • Python installation
  • Package managers (pip)
  • IDEs and editors (VS Code, PyCharm, Jupyter)
  • Basic Git for development

Programming Fundamentals

  • Algorithms and programming logic
  • Pseudocode
  • Flowcharts

Best Practices

  • PEP 8 and code style
  • Naming conventions
  • Documentation and comments

🧱 Python Fundamentals

Data Types and Variables

  • Numbers (int, float, complex, decimal)
  • Strings and text manipulation
    • String methods
    • Formatting (f-strings, .format())
    • Basic regular expressions
  • Booleans and logical operators
  • None and its usage

Data Structures

  • Lists and operations
  • Tuples and their immutability
  • Dictionaries and use cases
  • Sets and set operations
  • Comprehensions (list, dict, set)

Control Flow

  • Conditionals and operators
  • Loops (for, while, break, continue)
  • Match cases (Python 3.10+)

Functions

  • Definition and calls
  • Positional and named arguments
  • *args and **kwargs
  • Basic decorators
  • Type hints
  • Lambda functions
  • Closures and scope

Modules and Packages

  • Import and namespaces
  • Module creation
  • requirements.txt and setup.py

⚙️ Intermediate Programming

Advanced OOP

  • Classes and special methods
  • Multiple inheritance
  • Class and static methods
  • Properties and descriptors
  • Abstract Base Classes
  • Mixins and composition

Data Handling

  • Contexts (with)
  • Files and directories
  • JSON, CSV, XML
  • Serialization
  • SQLite and databases

Error Handling

  • try/except/finally
  • Creating exceptions
  • Logging and debugging

Functional Programming

  • map, filter, reduce
  • itertools and functools
  • Immutability
  • Advanced decorators

🎯 Advanced Python

Concurrency and Parallelism

  • GIL and its impact
  • Threading and race conditions
  • Multiprocessing
  • async/await
  • Queues and pools

Testing

  • Unit testing (pytest)
  • Mocking
  • Basic TDD
  • Coverage
  • Integration testing

Optimization

  • Profiling
  • Memory management
  • Caching
  • Generators and iterators

Typing and Quality

  • Advanced type hints
  • mypy
  • Linting (pylint, flake8)
  • Code quality tools

🧮 Advanced Data Structures and Algorithms

Advanced Data Structures

  • Trees (binary, AVL, etc.)
  • Graphs and their representations (adjacency matrix, adjacency lists)
  • Tries
  • Heaps and priority queues

Advanced Algorithms

  • Advanced search and sorting (mergesort, quicksort)
  • Graph algorithms (Dijkstra, A*)
  • Dynamic programming
  • Greedy algorithms

Theoretical Concepts

  • Algorithmic complexity (Big O notation)
  • Recursion and memoization
  • Programming paradigms (functional, object-oriented, imperative)

📊 Data Science and ML

NumPy

  • Arrays and operations
  • Broadcasting
  • Numerical optimization

Pandas

  • DataFrames and Series
  • Data manipulation
  • Exploratory analysis

Visualization

  • Matplotlib
  • Seaborn
  • Plotly

Basic Machine Learning

  • Scikit-learn
  • Basic pipelines
  • Metrics and evaluation

🌐 Web Development

FastAPI

  • REST APIs
  • Pydantic
  • OpenAPI/Swagger
  • Authentication

Django

  • MVT pattern
  • ORM
  • Admin interface
  • Templates

Flask

  • Routing
  • Templates
  • Extensions

🔒 Security

Web Development Security

  • Common attacks: XSS, CSRF, SQL Injection
  • Secure password handling
  • Sensitive data protection

🛠️ Practical Projects

CLI Applications

  • Click/Typer
  • Rich for TUIs

Desktop Applications

  • PyQt/PySide
  • Tkinter

Automation

  • Web scraping (BeautifulSoup, Selenium)
  • Task automation
  • Bots and scripts

Basic DevOps

  • Docker
  • Basic CI/CD
  • Deployment
  • Basic monitoring

Get Ready to Master Python!

Throughout this course, you will learn in a practical and effective way, with examples, exercises, and projects that will allow you to consolidate your knowledge and skills. Let’s start this journey together!