Python : Advance Course

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Rs. 30,000.00 Rs. 10,000.00 SAVE 67%
Learning Format: Live Training

What will be Cover ?

Module 17 – Concurrency & Parallelism
  • Threading
  • Multiprocessing
  • Asyncio
  • GIL Explained
  • Choosing the Right Approach
Module 18 – Memory Management & Performance
  • Python Memory Model
  • Reference Counting
  • Garbage Collection
  • Performance Bottlenecks
  • Profiling Tools
Module 19 – Design Patterns in Python
  • Singleton Pattern
  • Factory Pattern
  • Strategy Pattern
  • Observer Pattern
  • Pythonic Patterns vs Java Patterns
Module 20 – Architecture & Code Organization
  • Clean Architecture in Python
  • Package-Based Design
  • Dependency Management
  • Configuration-Driven Systems
Module 21 – Testing & Quality
  • Unit Testing (unittest
  • pytest)
  • Mocking
  • Test Organization
  • Code Coverage
  • Quality Engineering Mindset
Module 22 – Security
  • Secure Coding Practices
  • Input Validation
  • Authentication
  • Authorization Basics
  • Common Vulnerabilities
Module 23 – DevOps & Deployment
  • Packaging Python Applications
  • Virtual Environments in Production
  • Dockerizing Python Apps
  • CI/CD Basics
  • Logging
  • Monitoring
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Course Detail

The Python Advanced course is designed for experienced developers who want to master high-performance, scalable, and production-ready Python systems. This course goes beyond syntax and focuses on architecture, optimization, concurrency, security, and deployment practices used in real-world engineering environments.

Participants will explore advanced concepts such as multithreading, multiprocessing, asyncio, and concurrency patterns while understanding Python’s GIL and performance trade-offs. The course dives deep into memory management, garbage collection, profiling tools, and performance optimization techniques to build efficient applications.

Learners will study design patterns in Python, clean architecture principles, modular system design, and dependency management strategies. The course also emphasizes testing practices (pytest, mocking, coverage), security fundamentals, authentication and authorization mechanisms, and protection against common vulnerabilities.

In addition, students will learn DevOps practices including packaging applications, containerization with Docker, CI/CD basics, logging, monitoring, and production deployment strategies.

By the end of this course, participants will be capable of designing scalable backend systems, optimizing performance-critical applications, implementing secure coding standards, and deploying Python services confidently in production environments.

This course is ideal for backend developers, automation engineers, data engineers, and software professionals aiming for senior-level or system architecture roles.