Python Intermediate

Course Overview

Elevate your Python skills with our `Python Intermediate` course. This course is designed to bridge the gap between basic knowledge and advanced applications, providing you with a deeper understanding of Python programming and practical problem-solving abilities.

Who is this course for

This course is ideal for software developers, data analysts, and IT professionals who have basic Python knowledge and are looking to deepen their understanding and proficiency. A fundamental understanding of Python syntax, data types, and basic programming concepts is recommended.

Key Benefits

- Master intermediate Python skills to boost your programming skills.

- Gain practical experience with real-world projects to apply your knowledge
effectively.

- Improve your ability to write clean, maintainable code with best practices.

- Enhance your problem-solving skills by working with complex data structures
and algorithms.

- Learn to create and manage Python modules and packages for better code organization.

- Learn the Pythonic way of coding to write more efficient and readable code.

Learning Objectives

By the end of this course, you'll be able to:

- Create and manage Python modules and packages effectively

- Utilize nested data structures and algorithms in Python

- Learn how to structure and document your Python projects

- Implement object-oriented programming (OOP) principles effectively
- Manage and manipulate data using Python’s powerful libraries

- Develop and debug Python applications using best practices

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Format

Prerequisites

Course contents

Day 1: Establishing Intermediate Python skills

Module 1: Creating and Working with Modules

- Understanding Python modules

- Creating and importing custom modules

- Organizing code with modules

- Best practices for module management

Module 2: Creating and Working with Packages

- Difference between modules and packages

- Structuring and creating packages

- Using `__init__.py` and namespace packages

- Packaging and distributing Python code

Module 3: Object-Oriented Programming (OOP) in Python

- Class concepts

- Inheritance, polymorphism, and encapsulation

- Magic methods and operator overloading

- Creating and managing custom exceptions

Module 4: Nested Data Structures

- Understanding nested lists and dictionaries

- Manipulating complex data structures

- Accessing and modifying nested elements

- Best practices for working with nested data

Day 2: Practical Python Enhancements

Module 5: Testing and Mocking

- Writing unit tests with `unittest` and `pytest`

- Mocking objects the `unittest.mock` module

- Test coverage and best practices

- Python versions compatibility testing

Module 6: Error Handling

- Exception hierarchy in Python

- `try`, `except`, `else`, `finally` blocks

- Custom exceptions and raising exceptions

- Using logging for visibility into errors

Module 7: Python Debugging

- Debugging tools and techniques

- Using `pdb`, `ipdb` and other debuggers

- Analyzing stack traces and debugging common issues

- Profiling and optimizing code performance

Module 8: Iterators and Generators

- Understanding iterators and iterable objects

- Creating custom iterators

- Generator functions and expressions

- Using generators for efficient data processing

Day 3: Python Efficiency and Expertise

Module 9: Decorators and Context Managers

- Decorator patterns

- Writing and using decorators

- Context manager protocol and creating context managers

- Using `with` statement effectively

Module 10: Documenting Python Projects

- Importance of documentation

- Writing docstrings and comments

- Using `Material for MkDocs` generating documentation

- Best practices for maintaining documentation

Module 11: The Pythonic Way: Best Practices

- Writing clean and readable code

- Understanding and applying PEP 8 standards

- Code refactoring techniques

- Validating code with static analysis tools

Module 12: Python Program in a Docker Container

- Introduction to Docker and containerization

- Creating a Dockerfile for a Python application

- Building and running Docker containers

- Best practices for Dockerizing Python applications

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