Course Outline
Day 1 — Robust Python Foundations & Tooling
Modern Python Features and Typing
- Typing fundamentals, generics, Protocols, and TypeGuard
- Dataclasses, frozen dataclasses, and an overview of attrs
- Pattern matching (PEP 634+) and idiomatic implementation
Code Quality and Tooling
- Code formatters and linters: black, isort, flake8, ruff
- Static type checking using MyPy and pyright
- Pre-commit hooks and developer workflows
Project Management and Packaging
- Dependency management with Poetry and virtual environments
- Package layout, entry points, and versioning best practices
- Building and publishing packages to PyPI and private registries
Day 2 — Design Patterns & Architectural Practices
Design Patterns in Python
- Creational patterns: Factory, Builder, Singleton (Pythonic variants)
- Structural patterns: Adapter, Facade, Decorator, Proxy
- Behavioral patterns: Strategy, Observer, Command
Architectural Principles
- SOLID principles applied to Python codebases
- Hexagonal/Clean Architecture and system boundaries
- Dependency injection patterns and configuration management
Modularity and Reuse
- Designing library code versus application code
- APIs, stable interfaces, and semantic versioning
- Managing configuration, secrets, and environment-specific settings
Day 3 — Concurrency, Async IO, and Performance
Concurrency and Parallelism
- Threading fundamentals and the implications of the GIL
- Multiprocessing and process pools for CPU-bound tasks
- Choosing between concurrent.futures and multiprocessing
Async Programming with asyncio
- Async/await patterns, event loops, and cancellation
- Designing async libraries and ensuring interoperability with sync code
- IO-bound patterns, backpressure, and rate limiting
Profiling and Optimization
- Profiling tools: cProfile, pyinstrument, perf, memory_profiler
- Optimizing hot paths and utilizing C-extensions/Numba where suitable
- Measuring latency, throughput, and resource utilization
Day 4 — Testing, CI/CD, Observability, and Deployment
Testing Strategies and Automation
- Unit testing and fixtures with pytest; organizing tests
- Property-based testing with Hypothesis and contract testing
- Mocking, monkeypatching, and testing asynchronous code
CI/CD, Release, and Monitoring
- Integrating tests and quality gates into GitHub Actions/GitLab CI
- Creating reproducible containers with Docker and multi-stage builds
- Application observability: structured logging, Prometheus metrics, and tracing
Security, Hardening, and Best Practices
- Dependency auditing, SBOM basics, and vulnerability scanning
- Secure coding practices for input validation and secrets management
- Runtime hardening: resource limits, user permissions, and container security
Capstone Project & Review
- Team lab: designing and implementing a small service using course patterns
- Testing, type-checking, packaging, and setting up a CI pipeline for the project
- Final review, code critique, and development of an actionable improvement plan
Summary and Next Steps
Requirements
- Strong intermediate-level Python programming proficiency
- Working knowledge of object-oriented programming and basic testing
- Experience with command-line operations and Git
Audience
- Senior Python developers
- Software engineers accountable for Python code quality and architecture
- Technical leads and MLOps/DevOps engineers working with Python codebases
Testimonials (2)
everything was perfect
Florin Vrincianu
Course - Python Programming Fundamentals
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
