Python Course Overview

Welcome to the Technogic Python Course. This curriculum is meticulously designed to take you from the mechanical realities of code execution to advanced software engineering patterns.

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Phase 1: The Foundation (Mechanical Mastery)

Module 1: Environment and Setup

Introduction to Python: History, features, and core philosophy.
Installing Python: Setting up the language on your machine.
The Python Interpreter: Understanding the REPL (Read-Eval-Print Loop) and executing scripts.
Development Environment: Setting up IDLE or VS Code.
Virtual Environments: Using venv to manage dependencies.
Hello World: Writing the first program.

Module 2: Variables and Data Types

The Reference Model: Variables as labels pointing to objects.
Primitive Types: Integers, floats, booleans, and strings.
Operators: Arithmetic, Logical, Comparison, and Bitwise.
String Manipulation: Formatting, slicing, and methods.
Type Casting: Converting between types.

Module 3: Control Flow and Logic

Conditional Logic: if, elif, and else.
Pattern Matching: The match-case statement.
Iteration: for and while loops.
Loop Control: break, continue, and pass.
Advanced Loops: else clause and nesting.

Module 4: Data Structures

Lists: Creation, indexing, and mutability.
Tuples: Immutable sequences.
Dictionaries: Key-value pairs and lookups.
Sets: Unique elements and operations.
Comprehensions: List, Set, and Dictionary.

Phase 2: Structure and Modularity

Module 5: Functions

Defining Functions: Parameters and return values.
Arguments: Positional, keyword, *args, **kwargs.
Scope: The LEGB rule and variable lifetime.
Lambda Functions: Anonymous single-line functions.

Module 6: Modules and Packages

The Import System: Standard libraries.
Creating Modules: Structuring code files.
Packages: __init__.py and directory structures.
The Standard Library: os, sys, datetime.

Module 7: Error Handling & I/O

Exceptions: try, except, else, finally.
Raising Exceptions: Custom errors.
File Handling: Reading/Writing with open().
Data Serialization: JSON and CSV.
Context Managers: The with statement.

Phase 3: Object-Oriented Programming (OOP)

Module 8: Classes and Objects

Class Anatomy: __init__, self.
Attributes: Instance vs. Class variables.
Methods: Instance, Class, and Static methods.
Encapsulation: Naming conventions.

Module 9: Inheritance

Inheritance: Extending functionality.
Polymorphism: Overriding and interfaces.
Multiple Inheritance: MRO and Mixins.
ABCs: Abstract Base Classes.

Module 10: Magic Methods

String Representation: __str__ vs __repr__.
Operator Overloading: Custom +, -, etc.
Container Emulation: __len__, __getitem__.

Phase 4: Advanced Python Concepts

Module 11: Functional Features

Map, Filter, Reduce: Functional tools.
Iterators: The iterator protocol.
Generators: yield and lazy evaluation.
Decorators: Modifying behavior.

Module 12: Metaprogramming

Metaclasses: Customizing class creation.
Descriptors: Customizing attribute access.
Dynamic Execution: exec() and eval().

Module 13: Concurrency

Threading: I/O-bound tasks & GIL.
Multiprocessing: CPU-bound parallelism.
AsyncIO: async/await and event loops.

Module 14: Memory Management

Reference Counting: Object tracking.
Garbage Collection: Cycle detection.
Profiling: Optimization techniques.

Phase 5: Professional Development

Module 15: Best Practices

PEP 8: Style guide.
Documentation: Docstrings & Type Hints.
Testing: unittest and pytest.
Debugging: pdb and logging.
Packaging: setuptools and publishing.

Module 16: Specializations

Web Development: Django / Flask.
Data Science: NumPy, Pandas.
Automation: Regex, Scripting & Scraping.