Python Course Curriculum

For IT professionals, Python offers numerous advantages for automating tasks, managing systems, monitoring infrastructure, and integrating systems. It is not only useful for those directly involved in development or DevOps but is also a powerful tool for system administrators, network engineers, and anyone managing IT infrastructure. With its versatility, simplicity, and massive ecosystem, learning Python can significantly boost an IT professional’s productivity and effectiveness.

Module 1: An Introduction to Python for DevOps

• Why Python is popular in DevOps
• Setting up Python for development
• Python syntax and basic operations
• Installing and setting up Python and Virtual Environments (venv)

Module 2: Python Fundamentals for DevOps

• Working with data types (Strings, Lists, Dictionaries, Tuples, Sets)
• Handling input and output (command-line arguments, user inputs)
• String manipulation and formatting (essential for working with logs and configuration files)
• Basic operators and expressions

Module 3: Python Control Flow and Automation

• Conditionals (if, elif, else) for decision-making
• Loops (for, while) for automation and iterative tasks
• Exception handling (try, except, finally) for robust scripts
• Control flow: break, continue, assert
• Writing scripts for task automation and error handling

Module 4: Functions, Modules, and Scripting Best Practices

• Writing reusable functions for DevOps tasks (e.g., automating deployments, monitoring)
• Creating custom modules for better code organization
• Understanding and using lambda functions for concise code
• Using Python's built-in os, subprocess, and shutil modules for system scripting and automation
• Writing scripts that interact with APIs, systems, and cloud services

Module 5: Advanced Python: Managing Files and Directories

• File and directory manipulation with Python (os, pathlib, shutil)
• Reading and writing to files (use cases in log analysis, configuration management)
• Using with statements for resource management (handling files and network connections)
• Exception handling for file operations (e.g., handling missing files, permission errors)

Module 6: Working with Data Structures for Automation

• Efficient use of lists, dictionaries, and sets for managing data
• Working with JSON and YAML (commonly used in DevOps for configuration files)
• List comprehensions and dictionary comprehensions for concise data manipulation
• Using built-in libraries like json and yaml to handle configuration files

Module 7: Networking and Interfacing with External Systems

• Using Python's socket and requests modules for network communication
• Automation with HTTP requests (REST APIs, interacting with DevOps tools like Jenkins, GitHub, etc.)
• Interfacing with cloud APIs (AWS, Azure, GCP) using Python SDKs (e.g., boto3, azure-mgmt, google-cloud)

Module 8: DevOps Automation with Python (Automation Frameworks)

• Writing Python scripts to automate DevOps pipelines (CI/CD)
• Using paramiko for SSH automation and fabric for remote deployments
• Working with Docker containers using Python's docker module
• Introduction to Python's integration with Jenkins, GitLab CI, and other CI/CD tools

Module 9: Debugging, Testing, and Quality Assurance

• Writing unit tests with unittest and pytest
• Best practices for writing testable code in DevOps scripts
• Logging and debugging in Python with the logging module
• Code quality tools (e.g., pylint, flake8) for maintaining code standards

Module 10: Parallelism and Performance Optimization

• Using Python's threading, multiprocessing, and asyncio for concurrency and parallelism
• Optimizing performance for large-scale automation tasks
• Using time, cProfile, and memory_profiler for performance profiling and improvement

Module 11: Managing Cloud Resources with Python

• Using Python to manage cloud infrastructure (AWS, GCP, Azure)
• Scripting deployments and resource management with cloud SDKs
• Automating cloud service provisioning, scaling, and monitoring using Python
• Infrastructure as Code (IaC) concepts and Python-based tools

Module 12: Advanced Python for DevOps: Monitoring and Logging

• Integrating Python with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack)
• Working with log files, analyzing logs, and sending alerts via Python
• Automating server health checks and performance monitoring
• Writing scripts for resource utilization tracking (CPU, memory, disk, )