Advanced Python Training

RexGalaxy Academy's Advanced Python Training is a practical, project-based program designed for learners who already understand Python basics and want to become more confident in writing clean, reusable, professional and automation-ready Python code.

This course goes beyond syntax and simple programs. Students learn how to structure Python applications, use object-oriented programming properly, handle files and exceptions, create reusable modules, work with decorators and generators, process text with regular expressions, connect with databases and APIs, write tests, debug errors and build useful automation projects.

The training is designed for students, developers, data learners, testers, automation learners and working professionals who want to strengthen Python for real-world use. The focus is on writing maintainable code, understanding why Python behaves the way it does and applying advanced concepts in practical tasks instead of memorizing theory.

Students should be able to:

• Write cleaner and more reusable Python code using functions, OOP, modules and packages.

• Apply advanced Python concepts such as decorators, generators, iterators, context managers and comprehensions.

• Handle files, exceptions, logs, regular expressions, APIs and database connectivity in practical projects.

• Debug, test and organize Python applications using professional coding practices.

• Build automation scripts and portfolio-ready projects that demonstrate advanced Python confidence.

This module refreshes important Python fundamentals and shifts learners from beginner-style coding to structured, professional programming. Students revise syntax, logic building and core data types while learning how advanced Python is used in automation, backend scripting, data workflows and real projects.

• Revise Python installation, IDE setup, interpreter usage and project workflow.

• Refresh variables, data types, operators, input-output and control flow.

• Understand Python execution, indentation, readability and naming conventions.

• Practice lists, tuples, sets and dictionaries with real examples.

• Learn mutable vs immutable objects and why it matters in advanced coding.

• Understand references, object identity, memory behavior and common mistakes.

• Build confidence in writing clean, readable and reusable Python programs.

• Practice small coding problems to strengthen logic before advanced topics.

• Learn how professional Python projects are planned, structured and improved.

This module develops strong command over functions and Pythonic coding patterns. Students learn how to write reusable logic, pass data efficiently, use flexible arguments and create concise code using lambda functions and comprehensions without making programs confusing.

• Create reusable functions with parameters, return values and documentation.

• Understand local, global, nonlocal and built-in scope in Python programs.

• Use default arguments, keyword arguments, *args and **kwargs properly.

• Learn function references, higher-order functions and callback-style patterns.

• Apply lambda functions for compact logic in suitable scenarios.

• Use map, filter, zip, enumerate and sorted with custom logic.

• Write list, dictionary and set comprehensions for cleaner data processing.

• Avoid overcomplicated one-line code and focus on readable Pythonic style.

• Practice function-based mini tasks for validation, formatting and data filtering.

This module teaches object-oriented programming deeply, with a focus on real-world modeling and reusable code. Students learn how classes, objects, attributes and methods work, and how OOP helps organize larger applications with cleaner responsibilities.

• Understand classes, objects and the difference between blueprint and instance.

• Create instance variables, class variables, constructors and instance methods.

• Use self correctly and understand how Python binds methods to objects.

• Design classes for real scenarios such as users, products, students and orders.

• Apply encapsulation concepts using naming conventions and properties.

• Understand inheritance, parent-child classes and method overriding.

• Learn polymorphism through shared method behavior across different classes.

• Use composition to build flexible object relationships without overusing inheritance.

• Practice OOP mini projects to organize code into meaningful reusable components.

This module takes OOP beyond the basics and helps students understand how Python objects behave internally. Learners explore dunder methods, properties, static methods, class methods and design practices that make code easier to maintain and extend.

• Use __str__, __repr__, __len__, __eq__ and other magic methods effectively.

• Understand operator overloading and custom object behavior in Python.

• Create properties with getters and setters for controlled attribute access.

• Use static methods and class methods in suitable class design situations.

• Learn abstract classes and interfaces for predictable object contracts.

• Understand basic SOLID thinking in simple Python project design.

• Separate responsibilities across classes for cleaner architecture.

• Avoid common OOP mistakes such as god classes and unnecessary inheritance.

• Build a structured OOP project with multiple collaborating classes.

This module focuses on building reliable Python programs that can work with files, handle failures and record meaningful logs. Students learn how to read and write different file formats while managing errors gracefully instead of allowing programs to crash unexpectedly.

• Read and write text files using correct modes and encoding practices.

• Work with CSV, JSON and structured data files in practical tasks.

• Use with statements to handle files safely through context management.

• Understand common file errors and how to handle missing or invalid files.

• Use try, except, else and finally blocks properly.

• Create custom exceptions for clearer application-level error handling.

• Learn logging levels such as debug, info, warning, error and critical.

• Configure log files for debugging and production-style tracking.

• Build file-processing utilities with validation, exception handling and logs.

This module teaches students how to organize Python code like real projects instead of keeping everything in one file. Learners work with modules, packages, imports, virtual environments and dependency management so their applications become cleaner and easier to maintain.

• Understand modules, import styles and Python's module search behavior.

• Split large programs into multiple files with clear responsibility.

• Create packages using folders and __init__.py where required.

• Use built-in modules such as os, sys, pathlib, random and math.

• Understand third-party packages and how libraries improve development speed.

• Create and activate virtual environments for isolated project dependencies.

• Install, freeze and manage packages using pip and requirements files.

• Avoid dependency conflicts and understand why environment separation matters.

• Structure a professional Python project with folders, modules and reusable utilities.

This module introduces powerful advanced Python concepts that make programs more flexible, memory-efficient and elegant. Students learn when and why to use iterators, generators, decorators and context managers in practical development tasks.

• Understand iterable vs iterator and how loops work internally in Python.

• Create custom iterators using __iter__ and __next__ methods.

• Build generators using yield for memory-efficient data processing.

• Use generator expressions for large or continuous data workflows.

• Understand decorators as functions that enhance other functions.

• Create decorators for logging, timing, validation and access control examples.

• Learn nested functions, closures and how decorators preserve behavior.

• Use context managers to manage resources safely and cleanly.

• Practice advanced utilities that combine decorators, generators and context managers.

This module helps students handle common real-world data cleaning and text processing tasks using Python's standard tools. Learners work with regular expressions, date-time operations and utility functions needed in automation, reports, validation and backend workflows.

• Understand regular expression patterns, matching, searching and replacement.

• Use character classes, groups, quantifiers and anchors in regex patterns.

• Validate emails, phone numbers, IDs, codes and structured text formats.

• Extract useful information from logs, documents and raw text data.

• Work with date, time, datetime and timedelta objects.

• Format, parse and compare dates for real application needs.

• Use pathlib and os utilities for file paths and folder automation.

• Apply string methods and data transformation logic for cleaning tasks.

• Build practical scripts for text extraction, validation and report preparation.

This module connects Python with external systems, which is essential for real-world applications. Students learn how Python communicates with databases and web APIs, handles structured responses and performs common create, read, update and delete operations.

• Understand why applications need databases and APIs for real data workflows.

• Connect Python with relational databases using suitable connectors.

• Execute SQL queries, fetch results and handle database exceptions.

• Perform insert, update, delete and select operations from Python scripts.

• Understand commits, transactions and safe database handling practices.

• Work with REST APIs using requests and structured HTTP methods.

• Send GET and POST requests with parameters, headers and payloads.

• Parse JSON responses and handle API errors, timeouts and status codes.

• Build mini projects that fetch API data, store results and generate useful output.

This module introduces performance-related concepts in Python and helps students understand how to manage tasks more efficiently. Learners explore concurrency, parallelism and practical ways to improve scripts without blindly optimizing code.

• Understand process, thread, concurrency and parallelism at a practical level.

• Learn where Python multithreading is useful, especially for I/O-bound tasks.

• Create and manage threads for downloading, file work and background tasks.

• Understand multiprocessing for CPU-heavy operations and parallel execution.

• Learn the basics of Python's GIL and why it affects performance choices.

• Use queues and safe communication patterns between tasks.

• Measure execution time and identify slow parts of a program.

• Apply simple optimization techniques without reducing code readability.

• Build practical scripts that use threading or multiprocessing where appropriate.

This module focuses on professional development habits that make Python projects reliable and easier to improve. Students learn testing, debugging, clean coding and project organization practices that are expected in real development and automation roles.

• Understand why testing is important for confidence and long-term maintenance.

• Write basic unit tests for functions, classes and reusable components.

• Use assertions and test cases to verify expected program behavior.

• Debug programs using IDE tools, print debugging and structured inspection.

• Understand common Python errors and how to trace root causes.

• Apply clean code principles such as meaningful names and small functions.

• Format and organize code for readability and team collaboration.

• Create README files, comments and documentation where useful.

• Refactor messy scripts into cleaner project structures with reusable modules.

The final module brings together all advanced Python concepts through practical projects and career-focused preparation. Students build useful automation scripts and learn how to explain their code clearly in interviews, resumes and portfolio discussions.

• Build file automation scripts for organizing, renaming and processing folders.

• Create data extraction and reporting utilities using CSV, JSON and text files.

• Develop API-based mini projects that fetch and process live data.

• Build database-connected scripts for storing and retrieving records.

• Create decorator, generator and OOP-based utilities for portfolio practice.

• Learn how to present Python projects with proper structure and documentation.

• Practice common advanced Python interview questions and coding explanations.

• Understand real-world use cases in backend, automation, testing and data workflows.

• Prepare a final portfolio project that demonstrates advanced Python capability.